Frequency band switching method and device, terminal equipment and storage medium

Abstract

The invention relates to the field of communication, and discloses a frequency band switching method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring link performance data of each frequency band in a network environment in which the communication equipment is currently located, so as to predict link transmission quality of each frequency band in a future time period, and determining candidate frequency bands; monitoring and balancing channel loads on a plurality of communication channels in each physical link in each candidate frequency band; and obtaining spectrum data of each candidate frequency band to analyze a signal intensity distribution condition of each candidate frequency band, further determining an interference signal and an interference degree existing in each candidate frequency band, screening out an optimal frequency band from each candidate frequency band, and switching the communication equipment to the optimal frequency band. Through link performance prediction, the optimal frequency band is actively switched after active prediction, switching is prevented from being triggered after link quality deterioration, dynamic and intelligent frequency band switching is achieved, and communication reliability and user experience can be remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a frequency band switching method, apparatus, terminal equipment, and storage medium. Background Art

[0002] In wireless communications, communication devices (such as mobile phones, IoT devices, and Wi-Fi routers) often need to switch between multiple frequency bands (such as 2.4 GHz, 5 GHz, and 6 GHz) to address issues such as channel congestion, signal interference, and link quality degradation. However, traditional frequency band switching mechanisms cannot predict changing trends in link quality, and therefore typically trigger switching only after link quality deteriorates. This results in untimely switching and reduces communication stability and reliability. Summary of the Invention

[0003] In view of this, in order to solve the problem in the existing technology that wireless communications cannot predict the trend of link quality changes, resulting in untimely switching timing and reduced communication stability, the present application provides a frequency band switching method, device, terminal equipment and storage medium.

[0004] In a first aspect, the present application provides a frequency band switching method, comprising: Obtaining link performance data for each frequency band in a network environment currently located by the communication device, and predicting link transmission quality of each frequency band in a future time period based on the link performance data to determine a number of candidate frequency bands to be switched to; monitoring and adjusting channel loads on multiple communication channels in each physical link within each candidate frequency band to balance the loads on each communication channel; Acquire spectrum data of each candidate frequency band, and analyze the signal strength distribution of each candidate frequency band according to the spectrum data; According to the signal strength distribution, the interference signals and interference levels in each candidate frequency band are determined; and according to the interference level of each interference signal, the optimal frequency band is selected from each candidate frequency band to switch the frequency band of the current communication link of the communication device to the optimal frequency band.

[0005] In an optional embodiment, monitoring and adjusting the channel loads on multiple communication channels in each physical link in each candidate frequency band to balance the loads on each communication channel includes: Allocating an initial load to each communication channel in each of the physical links according to a weight ratio pre-allocated to each of the physical links; A dynamic balancing algorithm is adopted and, according to the link performance of each physical link, the weight ratio of each physical link is adjusted, and the load of each communication channel is evenly adjusted.

[0006] In an optional implementation, the analyzing of the signal intensity distribution of each candidate frequency band based on the spectrum data comprises: determining the mean and maximum of the power of all frequency points in each candidate frequency band based on the spectrum data, to calculate the power standard deviation of each candidate frequency band; the power standard deviation is used to reflect the signal fluctuation degree; based on the power standard deviation, analyzing the change relationship between the power and the frequency in each candidate frequency band, to obtain the signal intensity distribution of each candidate frequency band.

[0007] In an optional implementation, the determining of the interference signals and the interference degree in each candidate frequency band based on the signal intensity distribution comprises: using a machine learning classification model to identify the spectrum features in the signal intensity distribution of each candidate frequency band, to determine the interference signals in each candidate frequency band and the interference intensity of each interference signal; matching the signal intensity of each interference signal with a preset interference intensity range, to obtain the interference level of each interference signal.

[0008] In an optional implementation, the method further comprises: if it is monitored that the current active state of the communication device is an inactive state, controlling the device to enter a low-power mode; after determining that the communication device enters the low-power mode, predicting an optimal wake-up time point by using a first prediction model trained in advance, to wake up the communication device at the optimal wake-up time point and exit the low-power mode.

[0009] In an optional implementation, the predicting of the link transmission quality of each frequency band in a future time period based on the link performance data comprises: inputting the link performance data of each frequency band into a second prediction model trained in advance, to output a prediction value of the data transmission quality of each frequency band in a future time period; the link performance data is used to represent the current link transmission quality of the corresponding frequency band; the training process of the second prediction model comprises: obtaining historical link performance data of each frequency band, pre-processing the historical link performance data, and using the pre-processed historical link performance data as training data to train a second prediction model, the second prediction model is used to predict the link performance of each frequency band in a future time period.

[0010] In an optional implementation, the screening of the optimal frequency band from each candidate frequency band comprises: The candidate frequency band with the lowest interference level of the interference signal is preferentially selected as the optimal frequency band; If the interference levels of the interference signals of the multiple candidate frequency bands are the same, the candidate frequency band satisfying the frequency band preference preset in the communication device is selected as the optimal frequency band.

[0011] In a second aspect, the present application provides a frequency band switching device, comprising: A candidate frequency band acquisition module is configured to acquire link performance data of each frequency band in a network environment in which a communication device is currently located, predict link transmission quality of each frequency band in a future time period according to the link performance data, and determine a plurality of candidate frequency bands available for switching; A load balancing module is configured to monitor and adjust channel load on a plurality of communication channels in each physical link in each candidate frequency band, so as to balance the load on each communication channel; A signal strength analysis module is configured to acquire frequency spectrum data of each candidate frequency band, and analyze signal strength distribution of each candidate frequency band according to the frequency spectrum data; A frequency band switching module is configured to determine interference signals and interference degrees existing in each candidate frequency band according to the signal strength distribution, select an optimal frequency band from each candidate frequency band according to the interference degree of each interference signal, and switch the frequency band of the communication link of the communication device to the optimal frequency band.

[0012] In a third aspect, the present application provides a terminal device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the frequency band switching method described above.

[0013] In a fourth aspect, the present application provides a computer storage medium storing a computer program, wherein the computer program is executed to implement the frequency band switching method described above.

[0014] The embodiments of the present application have the following beneficial effects: The embodiments of the present application provide a frequency band switching method, which makes the frequency band switching mechanism of a communication device change from passive switching to active prediction in a communication process, predicts the link quality change trend in advance, realizes forward-looking switching, avoids triggering switching after the link quality deteriorates, and thus can improve network stability, optimize the performance of a wireless communication link, and improve the stability, bandwidth utilization rate and anti-interference ability of data transmission, so as to realize dynamic and intelligent frequency band switching, and significantly improve communication reliability and user experience. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as limiting the scope of protection of the present application. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0016] Figure 1 A first flowchart of the frequency band switching method in the embodiments of the present application is shown; Figure 2 A second flowchart of the frequency band switching method in the embodiments of the present application is shown; Figure 3 A structural diagram of the frequency band switching device in the embodiments of the present application is shown. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0018] The components of the embodiments of the present application generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] In the following, the terms "include", "have", and their synonymous words used in various embodiments of the present application are only intended to represent specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be understood as first excluding the presence or adding the possibility of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.

[0020] In addition, the terms "first", "second", "third", etc. are only used for differentiation in description, and should not be understood as indicating or implying relative importance.

[0021] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present application.

[0022] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0023] Data traffic: the amount of data passing through a channel per unit time (such as throughput).

[0024] Number of active users: The number of users currently connected to the channel and communicating.

[0025] Channel utilization: The proportion of time a channel is used for data transmission.

[0026] The present application provides a frequency band switching method. Through link performance prediction, the frequency band switching mechanism of the communication equipment is changed from passive switching to active prediction during the communication process, the link quality change trend is predicted in advance, and the optimal frequency band is screened in advance to achieve frequency band switching, avoiding triggering switching after the link quality deteriorates, thereby improving the stability of communication.

[0027] For example, Figure 1 As shown, the method specifically includes the following steps: S110, obtaining link performance data of each frequency band in the network environment currently located by the communication device, and predicting the link transmission quality of each frequency band in a future time period based on the link performance data to determine several candidate frequency bands for switching.

[0028] In this embodiment, the built-in RF front-end of a communication device scans all available frequency bands (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.) in the current network environment and obtains link performance data for each frequency band. This link performance data is used to characterize the current link transmission quality of the corresponding frequency band. The communication device includes, but is not limited to, terminal devices (e.g., mobile phones), switching devices (e.g., routers), and servers.

[0029] Link performance data includes physical layer parameters and protocol layer parameters, including but not limited to signal strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER), data transmission rate, throughput, latency, and packet loss rate. In actual application scenarios, the required link performance parameters can be set based on actual needs.

[0030] In some examples, a prediction model is pre-trained, and a training process of the prediction model is as follows: historical link performance data of each frequency band is acquired, the historical link performance data is pre-processed, and the pre-processed historical link performance data is used as training data to train the second prediction model.

[0031] The pre-processing includes data cleaning and standardization processing; for example, outliers in the link performance data are removed first, and missing values are filled in by interpolation, and then the link performance data is normalized to achieve standardization. Further optionally, the link performance data after standardization can be aligned in time series to ensure that the timestamps of each link performance data are consistent, facilitating subsequent analysis and processing of the link performance data.

[0032] In an example, the historical link performance data after pre-processing can be converted into a time series format, and then divided into a training set, a validation set and a test set, and the parameters (such as the number of layers) of the long short-term memory network are set, the long short-term memory network is trained using the training set, and the validation set and the test set are used to verify and evaluate the prediction performance of the long short-term memory network, and finally the second prediction model with prediction result accuracy meeting the requirements is obtained.

[0033] Further, the second trained prediction model can be used to predict the link transmission quality of each frequency band in a future time period based on the current link performance data of each frequency band, and a frequency band with better link transmission quality can be selected as a candidate frequency band for subsequent communication process. The future time period can be one hour, one day, three days, etc., which can be set according to actual needs, and the present embodiment does not limit it.

[0034] That is, the link performance data of each frequency band currently acquired is input into the pre-trained second prediction model, and the second prediction model is used to predict the link performance of each frequency band in a future time period to output the predicted data transmission quality of each frequency band in the future time period.

[0035] Subsequently, according to the link quality score output by the second prediction model, a plurality of frequency bands with predicted data transmission quality in the front can be selected as candidate frequency bands for subsequent switching, and the number of candidate frequency bands is not limited; that is, the present embodiment predicts the link transmission quality of each frequency band in a future time period through a prediction model to select a plurality of candidate frequency bands for switching, which can predict the link deterioration trend in advance, avoid reselecting and switching frequency bands when the link quality decreases, improve the switching efficiency, and ensure the stability and reliability of the communication process.

[0036] S120 , monitoring and adjusting channel loads on multiple communication channels in each physical link within each candidate frequency band to balance the loads on each communication channel.

[0037] The channel load parameters of multiple communication channels in each physical link in each candidate frequency band are collected through the physical layer and the media access control layer. The channel load parameters are used to characterize the signal usage status. The channel load parameters include but are not limited to data traffic, number of active users and channel utilization.

[0038] In one embodiment, an initial load is allocated to each communication channel in each physical link based on a pre-assigned weight ratio of each physical link; a dynamic balancing algorithm is used to adjust the weight ratio of each physical link and the load of each communication channel is evenly adjusted based on the link performance of each physical link.

[0039] Among them, a static weight can be assigned to each physical link in advance. The weight value can be set and adjusted based on parameters such as link bandwidth capacity and operator priority; then the initial load is evenly distributed for each communication channel of each physical link based on the weight ratio.

[0040] Then, during communication, the link performance parameters of each physical link (including latency, packet loss rate, available bandwidth, and other parameters) are collected in real time. Each link performance parameter is used as a performance indicator to analyze the current link performance of each physical link. Based on the parameter values ​​of each performance indicator, a comprehensive performance score for each link is calculated. The weight of each physical link is adjusted based on this comprehensive performance score. The load is then redistributed based on this adjusted weight. This load redistribution process is similar to the initial load distribution process and is not detailed here.

[0041] This embodiment obtains the link performance of each physical link in real time to balance the channel load, thereby ensuring that the load of the communication channel remains balanced when the performance of different links changes, avoiding single-point overload and improving communication reliability. In addition, balancing the load before switching the frequency band can avoid the situation where the performance of the switched frequency band may degrade again due to uneven load, thereby effectively ensuring the stability of communication.

[0042] S130 , obtaining spectrum data of each candidate frequency band, and analyzing the signal strength distribution of each candidate frequency band according to the spectrum data.

[0043] Obtain the power value of each frequency point in the same frequency range of each candidate frequency band to obtain the original spectrum data, then convert the original spectrum data into structured data, and divide the structured data according to the candidate frequency band to ensure that each frequency point falls into its corresponding frequency band, so as to facilitate subsequent processing of the spectrum data.

[0044] As an optional implementation, as shown in Figure 2 The signal intensity distribution of each candidate frequency band is analyzed according to the spectrum data, including the following steps: S210, the mean and maximum of the power of all frequency points in each candidate frequency band are determined according to the spectrum data, so as to calculate the power standard deviation of each candidate frequency band.

[0045] For each candidate frequency band, the mean and maximum of the power of all frequency points in the candidate frequency band are calculated according to the spectrum data; and then the power standard deviation is calculated according to the mean and maximum.

[0046] The power standard deviation is used to reflect the degree of signal fluctuation; if the power standard deviation is large, it indicates that there is obvious power fluctuation or interference in the frequency band; if the power standard deviation is small, it indicates that the signal power distribution in the frequency band is relatively uniform.

[0047] S220, based on the power standard deviation, the change relationship between the power and the frequency in each candidate frequency band is analyzed, and the signal intensity distribution of each candidate frequency band is obtained.

[0048] In this embodiment, the frequency points in each candidate frequency band are arranged in frequency order to construct a curve graph of the power change with the frequency, and the stability of each candidate frequency band can be determined based on the curve graph. If the fluctuation of the curve graph of the corresponding candidate frequency band is gentle, it indicates that the power change of the frequency band is stable; if the fluctuation of the curve graph is obvious, it indicates that the frequency band may have interference. Therefore, this embodiment can identify the frequency band with large signal fluctuation or interference in each candidate frequency band by constructing the curve graph of the power change with the frequency of each candidate frequency band, and provide a basis for subsequent interference identification.

[0049] Further, the power values of each frequency band are histogram counted, and the signal intensity distribution results of each candidate frequency band are determined in combination with the distribution form of the histogram, the power standard deviation, and the power fluctuation.

[0050] S140, the interference signals existing in each candidate frequency band and the interference degree are determined according to the signal intensity distribution, and the optimal frequency band is selected from the candidate frequency bands according to the interference degree of each interference signal, so as to switch the frequency band of the current communication link of the communication device to the optimal frequency band.

[0051] In this embodiment, according to the signal intensity distribution, the frequency band with the smallest interference and the best channel quality is selected as the optimal frequency band to be switched from the multiple candidate frequency bands.

[0052] Exemplarily, the frequency spectrum features in the signal intensity distribution of each candidate frequency band are identified by using a machine learning classification model to determine the interference signals present in each candidate frequency band and the interference strength of each interference signal; the signal intensity of each interference signal is matched with a preset interference strength range to obtain the interference level of each interference signal.

[0053] The frequency spectrum features are extracted from the signal intensity distribution of each candidate frequency band, and the frequency spectrum features include but are not limited to frequency domain features (such as spectral centroid, bandwidth, harmonic component, etc.), time-frequency features (energy distribution, transient pulse feature), and interference features (such as continuous narrowband signal, power surge in a specific frequency band). The feature quantities of the frequency spectrum features can be set according to actual needs, and the embodiment is not limited in this regard.

[0054] Further, the frequency spectrum features of each candidate frequency band are classified and learned by using the machine learning model to output the interference signal categories in each candidate frequency band and the interference strength of the interference signals, and then the interference strength of each interference signal is matched with a preset range to determine the interference strength level (i.e., the interference degree). The machine learning model includes but is not limited to any model such as a random forest model and a support vector machine model.

[0055] Further, the embodiment can select the optimal frequency band according to the interference strength levels of the candidate frequency bands; wherein, the candidate frequency band with the lowest interference level of the interference signals is prioritized as the optimal frequency band; if the interference levels of the interference signals of multiple candidate frequency bands are the same, the candidate frequency band that meets the pre-set frequency band preference in the communication device is selected as the optimal frequency band.

[0056] The embodiment determines the interference signals and the interference degree in each candidate frequency band, so that the optimal frequency band available for switching can be dynamically adjusted according to the interference strength level, providing a quantitative basis for the influence of interference on frequency band selection.

[0057] As an optional implementation, the embodiment can also monitor the current state of the communication device in real time during the communication process to automatically control the power consumption mode of the communication device and improve energy efficiency.

[0058] If the current active state of the communication device is determined to be an inactive state, the device is controlled to enter a low-power consumption mode; after determining that the communication device enters the low-power consumption mode, a first prediction model pre-trained is used to predict the optimal wake-up time point to wake up the communication device at the optimal wake-up time point and exit the low-power consumption mode.

[0059] In the embodiment, the data traffic of the application layer is monitored to determine the current active state of the device according to the data traffic; if the data traffic is less than a preset threshold, it indicates that the active state of the device is an inactive state, and then the device is controlled to enter a low-power mode. Otherwise, if the data traffic is greater than or equal to the preset threshold, it indicates that the device is currently in an active state, and the current power consumption mode is maintained.

[0060] In some examples, the first prediction model is pre-trained to predict the start time of the next active period, so as to determine the optimal wake-up time point of the device according to the start time of the next active period, and to wake up the communication device at the optimal wake-up time point to exit the low-power mode.

[0061] The first prediction model can be trained by using a lightweight model such as a recurrent neural network, and the training process can be set and adjusted according to actual needs, which is not limited in the embodiment.

[0062] Obviously, the embodiment can avoid resource waste caused by frequent wake-up by waking up the device at the appropriate time, maximize energy saving effect while ensuring communication response capability, and improve energy efficiency.

[0063] The embodiment uses a machine learning model to predict the link quality of the frequency band, realizes forward-looking frequency band selection, improves the utilization rate of channel resources by dynamically adjusting the weights of each physical link, identifies interference signals combined with machine learning and spectrum feature, and performs grade division, comprehensively considers the interference grade and device preference, and improves the rationality of switching decision; and can also control the device to enter a low-power mode when the device is in an inactive state, and predict the wake-up time point to improve energy efficiency. The embodiment combines dynamic load balancing, machine learning prediction, spectrum feature identification, interference grade evaluation and other key technologies, is suitable for various wireless communication scenarios, and has good practicability.

[0064] As shown in Figure 3 The present application provides a frequency band switching device, which comprises: A candidate frequency band acquisition module 310 is configured to acquire link performance data of each frequency band in a network environment in which a communication device is currently located, predict the link transmission quality of each frequency band in a future time period according to the link performance data, and determine a plurality of candidate frequency bands available for switching; A load balancing module 320 is configured to monitor and adjust the channel load on a plurality of communication channels in each physical link in each candidate frequency band to balance the load on each communication channel; A signal strength analysis module 330 is configured to acquire spectrum data of each candidate frequency band, and analyze the signal strength distribution of each candidate frequency band according to the spectrum data; The frequency band switching module 340 is configured to determine the interference signals and the interference degrees of the interference signals in each of the candidate frequency bands according to the signal strength distribution, and select an optimal frequency band from the candidate frequency bands according to the interference degrees of the interference signals, so as to switch the frequency band of the communication link of the communication device to the optimal frequency band.

[0065] It can be understood that the frequency band switching device in the embodiment corresponds to the frequency band switching method in the above-described embodiment, and the optional items in the above-described embodiment are also applicable to the embodiment, and thus will not be described herein again.

[0066] The present application further provides a computer device. Illustratively, the computer device comprises a processor and a memory. The memory stores a computer program. The processor executes the computer program, so that the computer device performs the functions of the above-described frequency band switching method or each module in the above-described frequency band switching device.

[0067] The processor can be an integrated circuit chip with a signal processing capability. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor, etc., and can implement or execute the disclosed methods, steps, and logic block diagrams in the embodiments of the present application.

[0068] The memory can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and the like. The memory is configured to store a computer program. After receiving an execution instruction, the processor can execute the computer program accordingly.

[0069] The application further provides a computer storage medium for storing the computer program used in the computer device.

[0070] In several embodiments provided in the application, it should be understood that the disclosed apparatus and method can also be implemented by other means. The apparatus embodiments described above are only illustrative, for example, the flowcharts and structural diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in alternative implementation manners, the functions annotated in the blocks can also occur in an order different from that annotated in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the structural diagram and / or flowchart, and the combination of blocks in the structural diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0071] In addition, each functional module or unit in the embodiments of the application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0072] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for causing a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the application.

[0073] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A frequency band switching method, characterized in that: include: Obtaining link performance data for each frequency band in a network environment currently located by the communication device, and predicting link transmission quality of each frequency band in a future time period based on the link performance data to determine a number of candidate frequency bands to be switched to; monitoring and adjusting channel loads on multiple communication channels in each physical link within each candidate frequency band to balance the loads on each communication channel; Acquire spectrum data of each candidate frequency band, and analyze the signal strength distribution of each candidate frequency band according to the spectrum data; According to the signal strength distribution, the interference signals and interference levels in each candidate frequency band are determined; and according to the interference level of each interference signal, the optimal frequency band is selected from each candidate frequency band to switch the frequency band of the current communication link of the communication device to the optimal frequency band.

2. The frequency band switching method according to claim 1, wherein: The monitoring and adjusting the channel loads on the multiple communication channels in each physical link in each candidate frequency band to balance the loads on the communication channels includes: Allocating an initial load to each communication channel in each of the physical links according to a weight ratio pre-allocated to each of the physical links; A dynamic balancing algorithm is adopted and, according to the link performance of each physical link, the weight ratio of each physical link is adjusted, and the load of each communication channel is evenly adjusted.

3. The frequency band switching method according to claim 1, wherein: Analyzing the signal strength distribution of each candidate frequency band according to the spectrum data includes: Determining the mean and maximum power of all frequency points in each candidate frequency band based on the spectrum data to calculate the power standard deviation of each candidate frequency band; the power standard deviation is used to reflect the degree of signal fluctuation; Based on the power standard deviation, the relationship between the power and the frequency in each candidate frequency band is analyzed to obtain the signal strength distribution of each candidate frequency band.

4. The frequency band switching method according to claim 1, wherein: The determining, according to the signal strength distribution, the interference signal and the interference degree in each candidate frequency band includes: Identifying spectral features in the signal strength distribution of each candidate frequency band using a machine learning classification model to determine interference signals present in each candidate frequency band and interference strength of each interference signal; The signal strength of each interference signal is matched with a preset interference strength range to obtain the interference level of each interference signal.

5. The frequency band switching method according to claim 1, wherein: The method further comprises: If it is detected that the current active state of the communication device is an inactive state, controlling the device to enter a low power consumption mode; After determining that the communication device enters the low power consumption mode, an optimal wake-up time point is predicted by using a pre-trained first prediction model, so as to wake up the communication device at the optimal wake-up time point and exit the low power consumption mode.

6. The frequency band switching method according to claim 1, wherein: The predicting, based on the link performance data, the link transmission quality of each of the frequency bands in a future time period includes: Inputting the link performance data of each frequency band into a pre-trained second prediction model to output a predicted value of the data transmission quality of each frequency band in a future time period; the link performance data is used to characterize the current link transmission quality of the corresponding frequency band; The training process of the second prediction model includes: Obtain historical link performance data of each of the frequency bands, preprocess the historical link performance data, and use the preprocessed historical link performance data as training predictions to train a second prediction model, which is used to predict the link performance of each of the frequency bands in a future time period.

7. The frequency band switching method according to claim 1 or 6, characterized in that: The selecting the optimal frequency band from the candidate frequency bands includes: Prioritizing the candidate frequency band with the lowest interference level of the interference signal as the optimal frequency band; If the interference levels of the interference signals of the plurality of candidate frequency bands are the same, the candidate frequency band that satisfies the frequency band preference preset in the communication device is used as the optimal frequency band.

8. A frequency band switching device, characterized in that: include: A candidate frequency band acquisition module is configured to acquire link performance data of each frequency band in the network environment currently located by the communication device, and predict the link transmission quality of each frequency band in a future time period based on the link performance data to determine a number of candidate frequency bands available for switching; a load balancing module, configured to monitor and adjust channel loads on a plurality of communication channels in each physical link within each candidate frequency band to balance the loads on each communication channel; a signal strength analysis module, configured to obtain spectrum data of each candidate frequency band and analyze the signal strength distribution of each candidate frequency band according to the spectrum data; The frequency band switching module is used to determine the interference signals and interference levels in each of the candidate frequency bands based on the signal strength distribution, and to select the optimal frequency band from each of the candidate frequency bands based on the interference level of each interference signal, so as to switch the frequency band of the current communication link of the communication device to the optimal frequency band.

9. A terminal device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the frequency band switching method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored therein, and when the computer program is executed, the frequency band switching method according to any one of claims 1 to 7 is implemented.

Images