A dual-channel automatic switching method for dual-mode multi-frequency internet of things communication

Abstract

The present application relates to the technical field of dual-channel automatic switching, and relates to a dual-channel automatic switching method for dual-mode multi-frequency Internet of Things communication.The method comprises the following steps: S1, obtaining a communication channel of a dual-mode Internet of Things, building a wireless network based on the communication channel, and performing high-performance to low-performance sorting according to the channel characteristics of the communication channel; S2, establishing a channel resource pool according to the sorted communication channel; the present application realizes quantitative characterization of the service transmission fluctuation characteristics by analyzing the fluctuation frequency of the historical service transmission volume of the Internet of Things and setting a fluctuation coefficient, simultaneously dynamically adjusts the time interception granularity according to the fluctuation coefficient, so that the analysis granularity of the service data can be adapted to the actual fluctuation, and a linear regression prediction model is constructed in combination with the time unit transmission volume and the extreme value, which greatly improves the accuracy of the service transmission volume prediction and provides reliable data support for subsequent channel selection and monitoring time period demarcation.

Description

Technical Field

[0001] This invention relates to the field of dual-channel automatic switching technology, and more specifically, to a dual-channel automatic switching method for dual-mode multi-frequency Internet of Things (IoT) communication. Background Technology

[0002] As a ubiquitous connectivity technology, the Internet of Things (IoT) uses a dual-mode, multi-frequency communication mode that is compatible with channel resources of different standards and frequency bands, effectively solving the problems of limited coverage and weak anti-interference capability of single-standard IoT.

[0003] Existing technologies mostly employ terminal-based switching decision logic, which is detached from the global resource scheduling of wireless networks. This easily leads to channel resource conflicts within the network caused by multiple terminals switching channels simultaneously. Furthermore, the fixed primary and backup channel mode cannot be dynamically adjusted according to the actual channel performance, resulting in low utilization of network channel resources and weakening the collaborative communication efficiency of IoT wireless networks. At the same time, the switching decision often relies on a single received signal strength indicator, which is easily misled by multipath fading and transient environmental interference, leading to problems such as ping-pong switching and false switching. Even if some technologies attempt to use multi-parameter decision-making, they do not take into account the fluctuation characteristics of service transmission volume for dynamic threshold adjustment. In scenarios with large fluctuations in service transmission volume, the accuracy of the decision is greatly reduced, which can easily lead to communication interruption or waste of channel resources. Therefore, a dual-channel automatic switching method for dual-mode multi-frequency IoT communication is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a dual-channel automatic switching method for dual-mode multi-frequency Internet of Things (IoT) communication, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, a dual-channel automatic switching method for dual-mode multi-frequency IoT communication is provided, comprising the following steps: S1. Obtain the communication channel of the dual-mode IoT, build a wireless network based on the communication channel, and sort the communication channels from high performance to low performance according to their channel characteristics. S2. Establish a channel resource pool based on the sorted communication channels, obtain historical business data and transmission volume of IoT, analyze the fluctuation frequency based on the transmission volume and set the fluctuation coefficient, then extract historical business data based on the fluctuation coefficient, extract the transmission volume of each time unit after extraction, summarize the highest and lowest values ​​of the transmission volume of each time unit to predict the transmission volume, and obtain the predicted total transmission volume. S3. Select the preferred communication channel from the resource pool based on the predicted total transmission volume, and the rest are backup communication channels. Determine the length of the monitoring period according to the fluctuation coefficient and divide the monitoring period into periods, and match the predicted transmission volume of each monitoring period. S4. Collect the real-time transmission volume of the Internet of Things, compare the difference between the real-time transmission volume and the predicted transmission volume in the same monitoring period, obtain the transmission volume difference, set the adjustment threshold according to the communication configuration of the preferred communication channel, and correct the adjustment threshold in combination with the transmission volume difference. S5. Obtain the transmission quality of the preferred communication channel, compare the transmission quality with the adjusted threshold, and start dual-frequency transmission of the backup channel when the quality is lower than the threshold. Select the backup communication channel with the same communication configuration as the preferred communication channel. S6. Compare the transmission quality of the preferred communication channel and the backup communication channel, and perform channel identity switching and maintain the original channel identity based on the comparison results.

[0006] As a further improvement to this technical solution, in step S1, the basic parameters of the dual-mode, multi-band communication channels supported by the dual-mode Internet of Things are collected, and the communication channels of the Internet of Things are fully acquired. A dual-mode IoT wireless network is built based on the acquired communication channels; The transmission rate, anti-interference capability, latency index, and coverage radius of each communication channel are extracted. After normalizing and evaluating each characteristic parameter, the communication channels are sorted from high performance to low performance according to the evaluation results.

[0007] As a further improvement to this technical solution, in step S2, a channel resource pool is established, and then the sorted communication channels are included in the channel resource pool, and the sorting level and performance attributes of each communication channel are marked. The historical service data of the physical network is obtained, and the transmission volume corresponding to each historical service data is broken down in the historical service data. The fluctuation frequency is analyzed in combination with the transmission volume of each historical service data to obtain the fluctuation frequency between adjacent historical service data. Then, the fluctuation coefficient is set according to the frequency amplitude of the fluctuation frequency. The higher the frequency amplitude of the fluctuation, the higher the fluctuation coefficient. Conversely, the lower the frequency amplitude of the fluctuation, the lower the fluctuation coefficient.

[0008] As a further improvement to this technical solution, the time granularity is set according to the fluctuation coefficient; The higher the volatility coefficient, the higher the granularity of the time cutoff. Conversely, the lower the fluctuation coefficient, the shorter the time truncation granularity; Historical business data is segmented into time units according to time granularity. Each time unit after segmentation is traversed, the business transmission volume within the corresponding time unit is extracted, and the highest peak value and lowest valley value of the transmission volume of all time units are counted. A transmission prediction model is constructed based on the transmission volume, highest value, and lowest value of each time unit. The predicted transmission volume of subsequent time units is obtained through model calculation and then summarized into the total predicted transmission volume.

[0009] As a further improvement to this technical solution, in step S3, the carrying capacity of the communication channel is analyzed based on the characteristic parameters of the communication channel to obtain the carrying capacity of each communication channel. Then, the predicted total transmission volume is combined with the carrying capacity of each communication channel for screening. Communication channels that match the predicted total transmission volume are selected, and the selected communication channels are used as preferred communication channels. The remaining communication channels in the channel resource pool, excluding the preferred communication channels, are classified as backup communication channels. In this process, the channel resource pool is selected according to the order of communication channels until it matches the predicted total transmission volume.

[0010] As a further improvement to this technical solution, in step S3, the length of the corresponding monitoring period is determined according to the fluctuation coefficient; The higher the fluctuation coefficient, the shorter the monitoring period. The lower the fluctuation coefficient, the longer the monitoring period. According to the defined monitoring period length, the overall prediction time corresponding to the total predicted transmission volume is divided into multiple consecutive monitoring periods of equal length. The predicted total transmission volume is allocated according to each monitoring period, and the predicted transmission volume corresponding to each monitoring period is obtained.

[0011] As a further improvement to this technical solution, in step S4, the communication interface of the IoT terminal is used to collect the service transmission volume in real time during the current monitoring period. The difference between the predicted transmission volume and the actual transmission volume under the same monitoring period is calculated to obtain the transmission volume difference. Then, the configuration parameters of the total channel of the preferred communication channel are extracted. Based on the configuration parameters, the initial basic switching threshold is set. The basic switching threshold is corrected according to the transmission volume difference to obtain the final applicable switching threshold. The larger the difference in transmission volume, the greater the correction magnitude for the basic handover threshold. The larger the difference in transmission volume, the smaller the correction range for the basic handover threshold.

[0012] As a further improvement to this technical solution, in step S5, the transmission quality of the preferred communication channel during the current monitoring period is collected, and the comprehensive evaluation value of the transmission quality is compared with the corrected switching threshold. When the overall transmission quality assessment value is lower than the switching threshold, the backup communication channel is immediately activated. The backup communication channel that matches the preferred channel data is selected from the channel resource pool. At the same time, the backup communication channel and the preferred communication channel work together to complete the dual-frequency parallel transmission of service data. Conversely, if the overall transmission quality assessment value is higher than the handover threshold, monitoring will continue.

[0013] As a further improvement to this technical solution, in step S6, the transmission quality of the backup communication channel is collected, and the transmission quality of the preferred communication channel and the backup communication channel is compared. When the transmission quality of the preferred communication channel is lower than that of the backup communication channel, the original backup communication channel is updated to the new preferred communication channel, and the original preferred communication channel is downgraded to the new backup communication channel, thus completing the channel identity switch. When the transmission quality of the preferred communication channel is higher than that of the backup communication channel, the original identities of the preferred and backup communication channels are maintained, and no channel identity switching is performed.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In this dual-channel automatic switching method for dual-mode multi-frequency IoT communication, the fluctuation frequency of historical IoT service transmission is analyzed and a fluctuation coefficient is set to achieve quantitative characterization of the fluctuation characteristics of service transmission. At the same time, the time truncation granularity is dynamically adjusted according to the fluctuation coefficient, so that the analysis granularity of service data can fit the actual fluctuation situation. Combined with the linear regression prediction model constructed by the transmission volume and extreme values ​​of time units, the accuracy of service transmission volume prediction is greatly improved, providing reliable data support for subsequent channel selection and monitoring period delineation. Moreover, the linear regression prediction model and various calculation methods take into account the low computing power characteristics of IoT terminals and gateways. The operation logic is simple and all calculations can be completed locally on the terminal or gateway without cloud collaboration, effectively reducing communication latency and improving the practicality and adaptability of the whole method in actual dual-mode multi-frequency IoT scenarios.

[0015] 2. In this dual-mode multi-frequency IoT communication dual-channel automatic switching method, a basic switching threshold is set based on the total configuration parameters of the preferred communication channel. Then, the basic switching threshold is dynamically corrected by combining the difference between the real-time transmission volume and the predicted transmission volume. This allows the switching threshold to adapt to the actual fluctuations in service transmission volume in real time, greatly improving the accuracy of switching decisions and effectively avoiding problems such as ping-pong switching and erroneous switching. At the same time, multiple dimensions of transmission quality parameters such as signal strength, bit error rate, packet loss rate, and transmission delay of the preferred channel are collected and comprehensively evaluated. The evaluated value is compared with the corrected switching threshold to determine whether to activate the backup channel. This makes the basis for switching decisions more comprehensive and objective, and effectively ensures the reliability of dual-channel switching decisions. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to the present invention. Figure 2This is a flowchart of S1 of the present invention; Figure 3 This is a flowchart of S2 of the present invention; Figure 4 This is a flowchart of S3 of the present invention; Figure 5 This is a flowchart of S4 of the present invention; Figure 6 This is a flowchart of S5 of the present invention; Figure 7 This is a flowchart of S6 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figures 1-7 As shown, the purpose of this embodiment is to provide a dual-channel automatic switching method for dual-mode multi-frequency IoT communication, including the following steps: S1. Obtain the communication channel of the dual-mode IoT, build a wireless network based on the communication channel, and sort the communication channels from high performance to low performance according to their channel characteristics. Establish the underlying foundation for wireless networking, complete channel assessment, networking, and priority ranking, and provide a benchmark for subsequent channel resource pooling and optimal channel selection; In S1, the basic parameters of the dual-mode, multi-band communication channels supported by the dual-mode IoT are collected, and the communication channels of the IoT are fully acquired. A dual-mode IoT wireless network is built based on the acquired communication channels; Clearly define the dual-mode IoT support for dual standards (such as NB-IoT+LoRa, 5G+Cat.1) and multiple frequency bands (such as 800M, 2.4G, 5.8G), delineate the channel acquisition frequency band range, avoid missed scans, and collect three types of basic parameters for each channel through IoT terminal radio frequency modules and spectrum analyzers (the optimal collection parameter set, taking into account both comprehensiveness and efficiency). A hybrid architecture is adopted, with star networking as the main method and mesh networking as backup. At the same time, all acquired communication channels are connected to the network architecture, and the binding of channels with gateways / nodes is completed. The network communication protocol is configured (adapting to dual standards and masking standard differences) to ensure that all channels can communicate and be scheduled normally within the network. Extract channel characteristic parameters for each communication channel, such as transmission rate (the amount of data that can be transmitted per unit time), anti-interference capability (the ability of the channel to resist co-channel / adjacent channel interference), latency index (the average latency of data transmission from the terminal to the gateway), and coverage radius (the effective communication coverage range of the channel). After normalizing and evaluating each characteristic parameter, rank the communication channels from high performance to low performance according to the evaluation results.

[0019] The above four types of parameters for each channel are collected in real time using a network testing tool. Each parameter is collected five times, and the average value is taken as the final characteristic parameter of the channel. It is clear that all four types of parameters are positive indicators (the larger the parameter value, the better the channel performance), and no reverse correction is needed. Then, the four types of characteristic parameters of each channel are linearly normalized to map the parameter values ​​to the [0,1] interval to eliminate the difference in dimensions. The comprehensive performance score of each channel is calculated by using the equal weight comprehensive scoring method. The higher the score, the better the channel performance.

[0020] S2. Establish a channel resource pool based on the sorted communication channels, obtain historical business data and transmission volume of IoT, analyze the fluctuation frequency based on the transmission volume and set the fluctuation coefficient, then extract historical business data based on the fluctuation coefficient, extract the transmission volume of each time unit after extraction, summarize the highest and lowest values ​​of the transmission volume of each time unit to predict the transmission volume, and obtain the predicted total transmission volume. Quantify business volatility, use volatility patterns to drive time slicing and traffic prediction, and provide data basis for subsequent selection of channel bandwidth and monitoring period density. In S2, a channel resource pool is established, and then each sorted communication channel is included in the channel resource pool, marking the sorting level and performance attributes of each communication channel. Create a unified channel resource pool, and enter all communication channels sorted from high performance to low performance into the resource pool in order of sorting, thus completing the full inclusion of channels; The historical service data of the physical network is obtained, and the transmission volume corresponding to each historical service data is broken down in the historical service data. The fluctuation frequency is analyzed in combination with the transmission volume of each historical service data to obtain the fluctuation frequency between adjacent historical service data. Then, the fluctuation coefficient is set according to the frequency amplitude of the fluctuation frequency. The higher the frequency amplitude of the fluctuation, the higher the fluctuation coefficient. Conversely, the lower the frequency amplitude of the fluctuation, the lower the fluctuation coefficient. The steps are as follows: Extract historical IoT business data within the period (e.g., the past 30 days), break down the single-moment transmission volume corresponding to each historical business data according to the timestamp and business type dimensions, form a dataset corresponding to timestamp and transmission volume, and sort all transmission volume data in ascending order of timestamp to ensure data continuity and no time gaps. The number of fluctuations in transmission volume per unit time was counted, and the fluctuation frequency was divided into 5 levels (level 1-5, level 1 = low frequency, level 5 = high frequency). The higher the level, the more severe the transmission volume fluctuation. Then, a linear mapping method was used to map the frequency level to a fluctuation coefficient in the 0-1 range, as shown in the following formula: ; in, For the frequency of transmission fluctuation, The absolute value of the difference between adjacent transmission volumes is greater than the minimum fluctuation threshold. (0.1Mbps) times, This represents the total number of data pairs (the number of adjacent data pairs). ; in, For fluctuation coefficient, This represents the minimum historical fluctuation frequency. This represents the maximum historical fluctuation frequency. The time granularity is set based on the fluctuation coefficient; The higher the volatility coefficient, the higher the granularity of the time cutoff. Conversely, the lower the fluctuation coefficient, the shorter the time truncation granularity; Historical business data is segmented into time units according to time granularity. Each time unit after segmentation is traversed, the business transmission volume within the corresponding time unit is extracted, and the highest peak value and lowest valley value of the transmission volume of all time units are counted. A transmission prediction model is constructed based on the transmission volume, highest value, and lowest value of each time unit. The predicted transmission volume of subsequent time units is obtained through model calculation and summarized into the total predicted transmission volume. Based on time units representing transmission volume, peak values, and trough values, a linear regression prediction model is constructed. The model is trained using historical time unit data to determine the model coefficients. Then, the number of time units for the period to be predicted is input, and the predicted transmission volume for each unit is calculated. These are then summarized into the total predicted transmission volume, as shown in the following formula: ; in, To predict the total transmission volume, The number of time units for the period to be predicted. Let be the historical average transmission volume of the i-th time unit to be predicted. and The coefficients obtained from model training. The average of historical extreme values ​​is used to correct prediction bias. The peak value of the transmission volume across all time units. The valley value of the transmission volume for all time units.

[0021] S3. Select the preferred communication channel from the resource pool based on the predicted total transmission volume, and the rest are backup communication channels. Determine the length of the monitoring period according to the fluctuation coefficient and divide the monitoring period into periods, and match the predicted transmission volume of each monitoring period. In S3, the carrying capacity of the communication channel is analyzed based on the characteristic parameters of the communication channel to obtain the carrying capacity of each communication channel. Then, the total predicted transmission volume is combined with the carrying capacity of each communication channel to filter the communication channels that match the total predicted transmission volume. The filtered communication channels are used as preferred communication channels, and the remaining communication channels in the channel resource pool other than the preferred communication channels are classified as backup communication channels. The selection of channel resources from the channel resource pool is based on the order of communication channels until it matches the predicted total transmission volume. The steps are as follows: The transmission rate, anti-interference capability, latency index, and coverage radius of each channel are extracted from the channel resource pool. The linear weighting method (optimal, efficient in calculation and closely matches the actual carrying characteristics) is used to map the characteristic parameters to the carrying capacity value. Consistent with the performance evaluation method for S1; The channels in the channel resource pool are sorted from high performance to low performance. Starting with the first-ranked channel, channels are selected sequentially until the total carrying capacity of the selected channels is greater than or equal to the predicted total transmission volume. The selected channels are marked as preferred communication channels (single channels are preferred, and multiple channels are selected when the carrying capacity is insufficient). At the same time, all available channels in the channel resource pool except for the preferred channels are uniformly marked as backup communication channels and marked with backup priority according to performance. In S3, the length of the corresponding monitoring period is determined based on the fluctuation coefficient; the fluctuation coefficient and the length of the monitoring period have an inverse linear relationship, and upper and lower limits for the length of the period are set (minimum 1 minute, maximum 60 minutes). The higher the fluctuation coefficient, the shorter the monitoring period. The lower the volatility coefficient, the longer the monitoring period, as shown in the formula below: in, For the length of the monitoring period, The threshold for the longest monitoring period. The threshold for the shortest monitoring period. This is the volatility coefficient; According to the defined monitoring period length, the overall prediction time corresponding to the total predicted transmission volume is divided into multiple consecutive monitoring periods of equal length. The predicted total transmission volume is allocated according to each monitoring period, and the predicted transmission volume corresponding to each monitoring period is obtained.

[0022] S4. Collect the real-time transmission volume of the Internet of Things, compare the difference between the real-time transmission volume and the predicted transmission volume in the same monitoring period, obtain the transmission volume difference, set the adjustment threshold according to the communication configuration of the preferred communication channel, and correct the adjustment threshold in combination with the transmission volume difference. In S4, the communication interface of the IoT terminal is used to collect the service transmission volume in real time during the current monitoring period. Call the IoT terminal communication interface to periodically collect the service transmission volume during the current monitoring period, and take the arithmetic average of all collection points during the current monitoring period as the actual transmission volume for that period. The difference between the predicted transmission volume and the actual transmission volume under the same monitoring period is calculated to obtain the transmission volume difference. Then, the configuration parameters of the total channel of the preferred communication channel are extracted. Based on the configuration parameters, the initial basic switching threshold is set. The basic switching threshold is corrected according to the transmission volume difference to obtain the final applicable switching threshold. The larger the difference in transmission volume, the greater the correction magnitude for the basic handover threshold. The larger the difference in transmission volume, the smaller the adjustment range for the basic handover threshold. The steps are as follows: The predicted transmission volume pre-allocated during the current monitoring period is retrieved, and the transmission volume difference is calculated using the absolute difference. At the same time, the difference is processed to be non-negative to ensure that the correction magnitude is a positive adjustment amount. Then, the total channel configuration parameters of the preferred communication channels are extracted from the channel resource pool, and the total channel configuration parameters are normalized to eliminate the difference in dimensions. The basic handover threshold is calculated using linear weighted summation. A correction coefficient is calculated based on the difference in transmission volume (the larger the difference, the larger the coefficient). This correction coefficient is then used to linearly adjust the base handover threshold, limiting the final handover threshold to a reasonable range and avoiding abnormal extreme values. The final handover threshold is then output for subsequent channel quality comparison. The formula is as follows: ; in, To determine the final applicable switching threshold, Based on the switching threshold, For correction factor, The correction amplitude coefficient is 0.2~0.5 to control the correction strength. S5. Obtain the transmission quality of the preferred communication channel, compare the transmission quality with the adjusted threshold, and start dual-frequency transmission of the backup channel when the quality is lower than the threshold. Select the backup communication channel with the same communication configuration as the preferred communication channel. In S5, the transmission quality of the preferred communication channel during the current monitoring period is collected, and the comprehensive evaluation value of the transmission quality is compared with the corrected switching threshold. During the current monitoring period, transmission quality parameters (signal strength, bit error rate, packet loss rate, and transmission delay) of the preferred communication channel are periodically collected. The collected parameters are then denoised (by taking the arithmetic mean of multiple collections) to ensure parameter stability and remove outliers. A linear weighted method is then used to calculate the comprehensive transmission quality assessment value, as shown in the following formula: ; in, This is a comprehensive evaluation value for transmission quality. This is the normalized value of the signal strength. This is the normalized value of the bit error rate. This is the normalized value for packet loss rate. This is the normalized value of transmission delay. , , , Weights corresponding to quality parameters; When the overall transmission quality assessment value is lower than the switching threshold, the backup communication channel is immediately activated. The backup communication channel that matches the preferred channel data is selected from the channel resource pool. At the same time, the backup communication channel and the preferred communication channel work together to complete the dual-frequency parallel transmission of service data. Conversely, if the overall transmission quality assessment value is higher than the handover threshold, monitoring will continue.

[0023] S6. Compare the transmission quality of the preferred communication channel and the backup communication channel, and perform channel identity switching and maintain the original channel identity based on the comparison results.

[0024] In S6, the transmission quality of the backup communication channel is collected, and the transmission quality of the preferred communication channel and the backup communication channel is compared. When the transmission quality of the preferred communication channel is lower than that of the backup communication channel, the original backup communication channel is updated to the new preferred communication channel, and the original preferred communication channel is downgraded to the new backup communication channel, thus completing the channel identity switch. When the transmission quality of the preferred communication channel is higher than that of the backup communication channel, the original identities of the preferred and backup communication channels are maintained, and no channel identity switching is performed.

[0025] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dual-channel automatic switching method for dual-mode multi-frequency Internet of Things (IoT) communication, characterized in that: Includes the following steps: S1. Obtain the communication channel of the dual-mode IoT, build a wireless network based on the communication channel, and sort the communication channels from high performance to low performance according to their channel characteristics. S2. Establish a channel resource pool based on the sorted communication channels, obtain historical business data and transmission volume of IoT, analyze the fluctuation frequency based on the transmission volume and set the fluctuation coefficient, then extract historical business data based on the fluctuation coefficient, extract the transmission volume of each time unit after extraction, summarize the highest and lowest values ​​of the transmission volume of each time unit to predict the transmission volume, and obtain the predicted total transmission volume. S3. Select the preferred communication channel from the resource pool based on the predicted total transmission volume, and the rest are backup communication channels. Determine the length of the monitoring period according to the fluctuation coefficient and divide the monitoring period into periods, and match the predicted transmission volume of each monitoring period. S4. Collect the real-time transmission volume of the Internet of Things, compare the difference between the real-time transmission volume and the predicted transmission volume in the same monitoring period, obtain the transmission volume difference, set the adjustment threshold according to the communication configuration of the preferred communication channel, and correct the adjustment threshold in combination with the transmission volume difference. S5. Obtain the transmission quality of the preferred communication channel, compare the transmission quality with the adjusted threshold, and start dual-frequency transmission of the backup channel when the quality is lower than the threshold. Select the backup communication channel with the same communication configuration as the preferred communication channel. S6. Compare the transmission quality of the preferred communication channel and the backup communication channel, and perform channel identity switching and maintain the original channel identity based on the comparison results.

2. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S1, the basic parameters of the dual-mode, multi-band communication channels supported by the dual-mode Internet of Things are collected, and the communication channels of the Internet of Things are fully acquired. A dual-mode IoT wireless network is built based on the acquired communication channels; The transmission rate, anti-interference capability, latency index, and coverage radius of each communication channel are extracted. After normalizing and evaluating each characteristic parameter, the communication channels are sorted from high performance to low performance according to the evaluation results.

3. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S2, a channel resource pool is established, and then each sorted communication channel is included in the channel resource pool, marking the sorting level and performance attributes of each communication channel. The historical service data of the physical network is obtained, and the transmission volume corresponding to each historical service data is broken down in the historical service data. The fluctuation frequency is analyzed in combination with the transmission volume of each historical service data to obtain the fluctuation frequency between adjacent historical service data. Then, the fluctuation coefficient is set according to the frequency amplitude of the fluctuation frequency. The higher the frequency amplitude of the fluctuation, the higher the fluctuation coefficient. Conversely, the lower the frequency amplitude of the fluctuation, the lower the fluctuation coefficient.

4. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 3, characterized in that: The time granularity is set based on the fluctuation coefficient; The higher the volatility coefficient, the higher the granularity of the time cutoff. Conversely, the lower the fluctuation coefficient, the shorter the time truncation granularity; Historical business data is segmented into time units according to time granularity. Each time unit after segmentation is traversed, the business transmission volume within the corresponding time unit is extracted, and the highest peak value and lowest valley value of the transmission volume of all time units are counted. A transmission prediction model is constructed based on the transmission volume, highest value, and lowest value of each time unit. The predicted transmission volume of subsequent time units is obtained through model calculation and then summarized into the total predicted transmission volume.

5. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S3, the carrying capacity of the communication channel is analyzed based on the characteristic parameters of the access channel to obtain the carrying capacity of each communication channel. Then, the predicted total transmission volume is combined with the carrying capacity of each communication channel to filter the communication channels that match the predicted total transmission volume. The filtered communication channels are used as preferred communication channels, and the remaining communication channels in the channel resource pool other than the preferred communication channels are classified as backup communication channels. In this process, the channel resource pool is selected according to the order of communication channels until it matches the predicted total transmission volume.

6. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S3, the length of the corresponding monitoring period is determined based on the fluctuation coefficient; The higher the fluctuation coefficient, the shorter the monitoring period. The lower the fluctuation coefficient, the longer the monitoring period. According to the defined monitoring period length, the overall prediction time corresponding to the total predicted transmission volume is divided into multiple consecutive monitoring periods of equal length. The predicted total transmission volume is allocated according to each monitoring period, and the predicted transmission volume corresponding to each monitoring period is obtained.

7. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S4, the communication interface of the IoT terminal is used to collect the service transmission volume in real time during the current monitoring period. The difference between the predicted transmission volume and the actual transmission volume under the same monitoring period is calculated to obtain the transmission volume difference. Then, the configuration parameters of the total channel of the preferred communication channel are extracted. Based on the configuration parameters, the initial basic switching threshold is set. The basic switching threshold is corrected according to the transmission volume difference to obtain the final applicable switching threshold. The larger the difference in transmission volume, the greater the correction magnitude for the basic handover threshold. The larger the difference in transmission volume, the smaller the correction range for the basic handover threshold.

8. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S5, the transmission quality of the preferred communication channel during the current monitoring period is collected, and the comprehensive evaluation value of the transmission quality is compared with the corrected switching threshold. When the overall transmission quality assessment value is lower than the switching threshold, the backup communication channel is immediately activated. The backup communication channel that matches the preferred channel data is selected from the channel resource pool. At the same time, the backup communication channel and the preferred communication channel work together to complete the dual-frequency parallel transmission of service data. Conversely, if the overall transmission quality assessment value is higher than the handover threshold, monitoring will continue.

9. The dual-channel automatic switching method for dual-mode multi-frequency IoT communication according to claim 1, characterized in that: In step S6, the transmission quality of the backup communication channel is collected, and the transmission quality of the preferred communication channel and the backup communication channel is compared. When the transmission quality of the preferred communication channel is lower than that of the backup communication channel, the original backup communication channel is updated to the new preferred communication channel, and the original preferred communication channel is downgraded to the new backup communication channel, thus completing the channel identity switch. When the transmission quality of the preferred communication channel is higher than that of the backup communication channel, the original identities of the preferred and backup communication channels are maintained, and no channel identity switching is performed.

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