A Bluetooth 4G dual-mode communication method for IoT devices

Abstract

This application provides a Bluetooth / 4G dual-mode communication method for IoT devices, relating to the field of IoT communication technology. The IoT device includes a control chip and a dual-communication module encompassing both Bluetooth and 4G communication. The method, applied to the control chip, includes: acquiring Bluetooth signal quality parameters (including RSSI, PLR, INT) and 4G signal quality parameters (including RSRP, SINR, BSL); calculating the Bluetooth signal score ScoreBLE and the 4G signal score Score4G; determining the target communication mode based on these scores; and controlling the switching of the dual-communication module's communication mode based on the target communication mode. The communication modes include Bluetooth primary mode, 4G primary mode, and Bluetooth / 4G dual-active mode. This solution facilitates smooth switching, effectively reduces the "ping-pong effect," and establishes a collaborative mechanism when dual-mode communication is simultaneously active, effectively leveraging the advantages of dual-mode communication and reducing bandwidth redundancy.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) communication technology, and more specifically, to a Bluetooth 4G dual-mode communication method for IoT devices. Background Technology

[0002] IoT devices widely employ wireless communication technologies (such as Bluetooth, Wi-Fi, 4G / 5G, etc.) for data transmission. Among these, Bluetooth (Bluetooth Low Energy, BLE) is commonly used for short-range device interconnection due to its low power consumption and low cost; while 4G networks, with their wide coverage and high bandwidth, are suitable for long-distance data transmission. However, existing technologies have many limitations.

[0003] Existing technical solutions and their problems in the market:

[0004] Option 1: Single communication mode dominates the market

[0005] Currently, most IoT devices on the market only use a single communication method (such as supporting only Bluetooth or only 4G). They generally suffer from the following problems:

[0006] (1) Poor scene adaptability: A single Bluetooth device completely fails when it is outside the coverage area, while a pure 4G device consumes too much power in close-range scenarios.

[0007] (2) Weak anti-interference capability: In complex environments (such as electromagnetic interference, mobile obstruction), the risk of interruption of a single communication link is extremely high.

[0008] Option 2: Dual-mode switching faces technical bottlenecks.

[0009] A few devices that support both Bluetooth and 4G dual-mode rely on simple rules (such as signal strength thresholds or fixed priorities) for their switching mechanism, and lack smooth transition capabilities, resulting in the following problems:

[0010] (1) Unsmooth handover: Hard handover causes brief communication interruptions (typical latency > 500ms), resulting in data packet loss or service interruption.

[0011] (2) Unintelligent decision-making: The handover is triggered based on a single parameter (such as Bluetooth RSSI) without considering comprehensive factors such as signal stability and network load, which can easily lead to the "ping-pong effect" (frequent handover).

[0012] (3) Waste of resources: When both modes are activated at the same time, there is a lack of coordination mechanism, resulting in redundancy in power consumption and bandwidth.

[0013] Summary of industry pain points:

[0014] In existing technologies, single-mode devices cannot meet both near-field and far-field requirements, while dual-mode devices have poor switching strategies and their actual performance has not met expectations. There is an urgent need for an intelligent communication solution that can evaluate multi-dimensional signal quality to achieve seamless switching between Bluetooth and 4G. Summary of the Invention

[0015] The purpose of this application is to provide a Bluetooth 4G dual-mode communication method for IoT devices. By evaluating multi-dimensional signal quality, a smart communication solution for seamless Bluetooth 4G switching can be achieved, effectively alleviating or even completely eliminating the aforementioned dual-mode switching problem.

[0016] To achieve the above objectives, the embodiments of this application are implemented in the following manner:

[0017] This application provides a Bluetooth / 4G dual-mode communication method for an IoT device. The IoT device is equipped with a control chip and a dual-communication module that includes Bluetooth and 4G communication. The method is applied to the control chip and includes: acquiring Bluetooth signal quality parameters and 4G signal quality parameters, wherein the Bluetooth signal quality parameters include RSSI, PLR, and INT, and the 4G signal quality parameters include RSRP, SINR, and BSL; calculating a Bluetooth signal score ScoreBLE based on the Bluetooth signal quality parameters, and calculating a 4G signal score Score4G based on the 4G signal quality parameters; determining a target communication mode based on the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, and controlling the communication mode switching of the dual-communication module based on the target communication mode, wherein the communication modes include Bluetooth main communication mode, 4G main communication mode, and Bluetooth / 4G dual-active mode. Bluetooth main communication mode indicates that Bluetooth is the current main link and 4G is in low-power standby mode; 4G main communication mode indicates that 4G is the current main link and Bluetooth maintains a heartbeat connection; and Bluetooth / 4G dual-active mode indicates that Bluetooth and 4G work simultaneously.

[0018] In conjunction with the first aspect, in a first possible implementation of the first aspect, calculating the Bluetooth signal score ScoreBLE based on Bluetooth signal quality parameters, and calculating the 4G signal score Score4G based on 4G signal quality parameters, includes:

[0019] The Bluetooth signal score ScoreBLE is calculated using the following formula:

[0020] ScoreBLE = w1*RSSI + w2*(1-PLR) + w3*(1 / INT),

[0021] Where ScoreBLE is the Bluetooth signal score, w1, w2 and w3 are the weighting coefficients of the Bluetooth signal quality parameters, RSSI is the received signal strength, PLR is the packet loss rate, and INT is the interference intensity.

[0022] The 4G signal score, Score4G, is calculated using the following formula:

[0023] Score4G = k1*RSRP + k2*SINR + k3*(1-BSL),

[0024] Wherein, Score4G is the 4G signal score, k1, k2 and k3 are the weighting coefficients of the 4G signal quality parameters, RSRP is the reference signal received power, SINR is the signal-to-noise ratio, and BSL is the base station load.

[0025] In conjunction with the first aspect, in the second possible implementation of the first aspect, the Bluetooth 4G dual-mode communication method for IoT devices operates in two modes: periodic operation and signal event-driven triggering operation. Based on the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, the target communication mode is determined, including: calculating the absolute difference D between the Bluetooth signal score ScoreBLE and the 4G signal score Score4G; obtaining the judgment threshold Δ for the current period based on the current communication mode; if ScoreBLE > Score4G and D > Δ, determining the Bluetooth main communication mode as the target communication mode; if Score4G > ScoreBLE and D > Δ, determining the 4G main communication mode as the target communication mode; if D ≤ Δ, predicting the future trend of the absolute difference D to obtain the predicted difference D', and determining the target communication mode based on the predicted difference D'.

[0026] In conjunction with the second possible implementation of the first aspect, in the third possible implementation of the first aspect, predicting the future trend of the absolute difference D to obtain the predicted difference D' includes: acquiring historical periodic data, wherein the historical periodic data includes Bluetooth signal quality parameters and 4G signal quality parameters of the previous n periods; determining Bluetooth prediction data based on the Bluetooth signal quality parameters of the previous n periods, wherein the Bluetooth prediction data includes RSSI', PLR', and INT'; determining 4G prediction data based on the 4G signal quality parameters of the previous n periods, wherein the 4G prediction data includes RSRP', SINR', and BSL'; and determining the predicted difference D' based on the Bluetooth prediction data and the 4G prediction data.

[0027] In conjunction with the third possible implementation of the first aspect, in the fourth possible implementation of the first aspect, Bluetooth prediction data is determined based on the Bluetooth signal quality parameters of the first n cycles, including:

[0028] RSSI', PLR', and INT' are calculated using the following formulas:

[0029] ,

[0030] ,

[0031] ,

[0032] Where RSSI' is the predicted received signal strength for the future period, PLR' is the predicted packet loss rate for the future period, and INT' is the predicted interference intensity for the future period. This represents the average received signal strength over the previous n periods. Let be the received signal strength in the first i-th period out of the first n periods. for The corresponding weights Let be the packet loss rate of the first period out of the first n periods. and These are the packet loss rates for the first i-th period and the (i+1)-th period, respectively, out of the first n periods. for The corresponding weights This represents the average interference intensity over the first n periods. Let represent the interference intensity in the first i-th cycle out of the first n cycles. for The corresponding weights This represents the maximum difference in interference intensity between two adjacent periods in the first n periods. For reference interference intensity difference, If the difference in interference intensity between two adjacent periods in the first n periods exceeds the reference interference intensity difference. And the cycle number of the most recent historical cycle. This represents the average interference intensity over the first m cycles. Let be the interference intensity in the j-th cycle out of the first m cycles. for The corresponding weights.

[0033] In conjunction with the third possible implementation of the first aspect, in the fifth possible implementation of the first aspect, 4G prediction data is determined based on the 4G signal quality parameters of the first n cycles, including:

[0034] RSRP', SINR', and BSL' are calculated using the following formulas:

[0035] ,

[0036] ,

[0037] ,

[0038] Where RSRP' is the signal quality parameter for the predicted future period, SINR' is the signal-to-noise ratio for the predicted future period, and BSL' is the base station load for the predicted future period. This represents the mean of the signal quality parameters over the first n periods. Let be the signal quality parameter for the first i-th period out of the first n periods. for The corresponding weights This represents the average signal-to-noise ratio over the first n periods. Let be the signal-to-noise ratio of the first i-th cycle out of the first n cycles. for The corresponding weights This represents the maximum signal-to-noise ratio difference between two adjacent cycles in the first n cycles. For reference, the signal-to-noise ratio is poor. The signal-to-noise ratio difference between two adjacent cycles in the first n cycles exceeds the reference signal-to-noise ratio difference. And the cycle number of the most recent historical cycle. This represents the average signal-to-noise ratio over the first p periods. Let be the interference intensity in the k-th cycle out of the first m cycles. for The corresponding weights This represents the base station load in the first period out of the previous n periods. and These represent the base station load in the first i-th period and the base station load in the (i+1)-th period, respectively. for The corresponding weights.

[0039] In conjunction with the third possible implementation of the first aspect, in the sixth possible implementation of the first aspect, the prediction difference D' is determined based on Bluetooth prediction data and 4G prediction data, including:

[0040] Based on RSSI', PLR', and INT' in the Bluetooth prediction data, weighting coefficients w1', w2', and w3' are determined, and the Bluetooth signal score ScoreBLE' for future periods is calculated using the following formula:

[0041] ScoreBLE' = w1'*RSSI' + w2'*(1-PLR') + w3'*(1 / INT'),

[0042] ScoreBLE' is the Bluetooth signal score for the future cycle;

[0043] Based on RSRP', SINR', and BSL' in the 4G prediction data, weighting coefficients k1', k2', and k3' are determined, and the 4G signal score Score4G' for the future period is calculated using the following formula:

[0044] Score4G' = k1'*RSRP'+ k2'*SINR'+ k3'*(1-BSL'),

[0045] Among them, Score4G' is the 4G signal score for the future period;

[0046] Calculate the predicted difference D' between the Bluetooth signal score ScoreBLE' for the future period and the 4G signal score Score4G' for the future period.

[0047] In conjunction with the third possible implementation of the first aspect, in the seventh possible implementation of the first aspect, the Bluetooth 4G dual-active mode includes Class I Bluetooth 4G dual-active mode, Class II Bluetooth 4G dual-active mode, and Class III Bluetooth 4G dual-active mode. Based on the prediction difference D', the target communication mode is determined, including: determining the prediction threshold Δ' based on RSSI', PLR', and INT' in the Bluetooth prediction data and RSRP', SINR', and BSL' in the 4G prediction data; if ScoreBLE'>Score4G' and D'>Δ', the target communication mode is determined. The target communication mode is determined to be Class I Bluetooth 4G dual-active mode. Class I Bluetooth 4G dual-active mode means that Bluetooth and 4G operate simultaneously, and data transmission uses Class I hybrid transmission mode. In Class I hybrid transmission mode, when Bluetooth transmits data fragments, data fragments transmitted by 4G are redundant at the end. If 4G completes data fragment transmission, Bluetooth transmission of the redundant portion is terminated; if 4G does not complete data fragment transmission, Bluetooth transmission of the redundant portion continues. If Score4G' > ScoreBLE' and D' > Δ', then Class II Bluetooth 4G dual-active mode is determined to be the target communication mode. In this context, Class II Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts Class II hybrid transmission mode. In Class II hybrid transmission mode, when 4G transmits data fragments, redundant data fragments are transmitted by Bluetooth at the end. If Bluetooth completes the data fragment transmission, 4G terminates the transmission of the redundant part; if Bluetooth does not complete the data fragment transmission, 4G continues the transmission of the redundant part. If D'≤Δ', Class III Bluetooth 4G dual-active mode is determined as the target communication mode. In this context, Class III Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts Class III transmission mode. Class III transmission mode means that Bluetooth and 4G use data fragment transmission.

[0048] In conjunction with the seventh possible implementation of the first aspect, in the eighth possible implementation of the first aspect, the judgment threshold Δ for the current period is obtained based on the current communication mode, including: if the current communication mode is Bluetooth main communication mode or 4G main communication mode, the set value is obtained as the judgment threshold Δ; if the current communication mode is Bluetooth 4G dual active mode, the prediction threshold of the previous period is obtained as the judgment threshold Δ.

[0049] Beneficial effects:

[0050] The IoT device is equipped with a control chip and a dual-communication module that includes Bluetooth and 4G communication. This solution provides a Bluetooth / 4G dual-mode communication method for IoT devices applied to the control chip. It acquires Bluetooth signal quality parameters (including RSSI, PLR, INT) and 4G signal quality parameters (including RSRP, SINR, BSL), calculates a Bluetooth signal score (ScoreBLE) based on the Bluetooth signal quality parameters, and a 4G signal score (Score4G) based on the 4G signal quality parameters. Based on this, the target communication mode is determined (Bluetooth primary mode, 4G primary mode, and Bluetooth / 4G dual-active mode. Bluetooth primary mode indicates that Bluetooth is the current main link and 4G is in low-power standby; 4G primary mode indicates that 4G is the current main link and Bluetooth maintains a heartbeat connection; Bluetooth / 4G dual-active mode indicates that Bluetooth and 4G work simultaneously). This solution sets three modes and designs an intelligent scoring mechanism. If a device has a significant advantage in a certain communication mode (e.g., Bluetooth or 4G) (its score is significantly higher than the other), single-mode communication is used to reduce power consumption. Furthermore, using the Bluetooth / 4G dual-active mode as a transition for mode switching facilitates smooth switching. The Bluetooth 4G dual-active mode is used in situations where the scores of Bluetooth and 4G communication are not significantly different (e.g., both scores are high or neither is high). It establishes a collaborative mechanism when dual-mode communication is activated simultaneously, which can effectively leverage the advantages of dual-mode communication and reduce bandwidth redundancy.

[0051] In Bluetooth 4G dual-active mode, a complete prediction, coordination, and switching mechanism is established, which can effectively leverage its smooth transition function. When a communication mode switch may occur in the predicted future period, partial redundancy is implemented in advance. This redundancy is dynamic and can make intelligent decisions based on the data transmission status of the redundant data (when the redundant party completes the data fragment transmission normally, the transmission of the redundant data fragment at the end is terminated in time, which can effectively save bandwidth redundancy; when the redundant party fails to complete the data fragment transmission normally, the transmission of the redundant data fragment at the end continues, which can ensure data integrity and effectively reduce data retransmission). This ensures reliable data transmission and saves bandwidth when unnecessary, achieving efficient coordination when Bluetooth 4G dual-mode communication is activated simultaneously, effectively leveraging the advantages of dual-mode communication.

[0052] In Bluetooth 4G dual-active mode, a sophisticated handover mechanism is designed. This mechanism predicts and subdivides Bluetooth 4G dual-active modes into three distinct modes to handle different predicted scenarios. By anticipating upcoming handovers and adapting to them using the corresponding data transmission mechanisms, a near-seamless handover can be achieved when the next cycle arrives. This avoids the unsmooth handover issues caused by existing dual-mode communication handover mechanisms (hard handovers cause brief communication interruptions, with typical latency >500ms, resulting in data packet loss or service disruption). In the prediction, based on Bluetooth signal quality parameters (including RSSI, PLR, INT) and 4G signal quality parameters (including RSRP, SINR, BSL) in complex scenarios (such as common mobile scenarios, which often cause significant fluctuations in one or more parameters), a parameter prediction scheme tailored to their characteristics was designed. (For different parameter characteristics, corresponding parameter prediction formulas were designed, and corresponding prediction mechanisms were specifically designed based on the possible abrupt changes of various parameters in mobile scenarios. Appropriate weights were assigned according to the proximity of historical periods, enabling the prediction accuracy to reach over 80%. The accuracy of the prediction significantly reduces the additional burden caused by redundancy in several types of Bluetooth 4G dual-active modes, while the overall computational load is not large and does not take much time.) Furthermore, in this case, a dynamic threshold adjustment mechanism was designed to handle special cases specially, making the switching mechanism more intelligent and helping to significantly reduce or even avoid the occurrence of the "ping-pong effect."

[0053] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating the Bluetooth 4G dual-mode communication method for IoT devices provided in this application embodiment.

[0056] Figure 2 The operation flow of the Bluetooth 4G dual-mode communication method for controlling the operation of IoT devices by the control chip.

[0057] Figure 3 This is a diagram illustrating historical cycles, current cycles, and future cycles. Detailed Implementation

[0058] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0059] Wireless communication is crucial for the interconnection of IoT devices. In this embodiment, the IoT device is equipped with a control chip and a dual-communication module that includes Bluetooth and 4G communication. This embodiment develops a Bluetooth / 4G dual-mode communication method for IoT devices to improve upon the shortcomings of existing dual-mode communication switching schemes on IoT devices. After the device is powered on, the control chip can run the Bluetooth / 4G dual-mode communication method of the IoT device through periodic event triggering (e.g., one cycle per second, one cycle per three seconds, etc.) and event-driven triggering (e.g., events such as before data transmission, signal abnormality alarms, etc.).

[0060] Please see Figure 1 and Figure 2 , Figure 1 A flowchart illustrating the Bluetooth 4G dual-mode communication method for IoT devices provided in this application embodiment; Figure 2 The operation flow of the Bluetooth 4G dual-mode communication method for controlling the chip to run IoT devices. The Bluetooth 4G dual-mode communication method for IoT devices may include steps S10, S20, and S30.

[0061] First, you can run step S10.

[0062] Step S10: Obtain Bluetooth signal quality parameters and 4G signal quality parameters, wherein the Bluetooth signal quality parameters include RSSI, PLR, and INT, and the 4G signal quality parameters include RSRP, SINR, and BSL.

[0063] In this embodiment, the control chip can acquire the current Bluetooth signal quality parameters and 4G signal quality parameters. The Bluetooth signal quality parameters include RSSI (Received Signal Strength Indicator), PLR (Packet Loss Rate), and INT (Interference Level), while the 4G signal quality parameters include RSRP (Reference Signal Received Power), SINR (Signal to Interference plus Noise Ratio), and BSL (Base Station Load).

[0064] After obtaining the Bluetooth signal quality parameters and the 4G signal quality parameters, the control chip can run step S20.

[0065] Step S20: Calculate the Bluetooth signal score ScoreBLE based on the Bluetooth signal quality parameters, and calculate the 4G signal score Score4G based on the 4G signal quality parameters.

[0066] In this embodiment, before calculating the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, the obtained parameters need to be normalized to map the original values ​​of RSSI, PLR, INT, RSRP, SINR, BSL, etc. to the [0,1] interval in order to unify the units.

[0067] For example, the normalization of RSSI:

[0068] , (1)

[0069] Normalization of INT:

[0070] , (2)

[0071] RSRP normalization:

[0072] , (3)

[0073] SINR normalization:

[0074] , (4)

[0075] BSL normalization:

[0076] , (5)

[0077] After normalization, the control chip can calculate the Bluetooth signal score ScoreBLE based on the Bluetooth signal quality parameters.

[0078] ScoreBLE = w1*RSSI + w2*(1-PLR) + w3*(1 / INT), (6)

[0079] Here, ScoreBLE is the Bluetooth signal score, and w1, w2, and w3 are the weighting coefficients for the Bluetooth signal quality parameters. Note that INT is not usually zero, so division by zero is not performed here.

[0080] Furthermore, the control chip can calculate the 4G signal score (Score4G) based on 4G signal quality parameters.

[0081] Score4G = k1*RSRP + k2*SINR + k3*(1-BSL), (7)

[0082] Among them, Score4G is the 4G signal score, and k1, k2 and k3 are the weighting coefficients of the 4G signal quality parameters.

[0083] After calculating the Bluetooth signal score ScoreBLE and the 4G signal score Score4G for the current period, the control chip can run step S30.

[0084] Step S30: Based on the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, determine the target communication mode, and control the switching of the communication modes of the dual communication modules based on the target communication mode. The communication modes include Bluetooth primary mode, 4G primary mode, and Bluetooth / 4G dual-active mode. Bluetooth primary mode indicates that Bluetooth is the current primary link and 4G is in low-power standby mode. 4G primary mode indicates that 4G is the current primary link and Bluetooth maintains a heartbeat connection. Bluetooth / 4G dual-active mode indicates that Bluetooth and 4G work simultaneously. While a sleep mode is not specifically mentioned here, it should not be considered as the absence of a sleep mode.

[0085] In this embodiment, the control chip can calculate the absolute difference D between the Bluetooth signal score ScoreBLE and the 4G signal score Score4G:

[0086] D=|ScoreBLE-Score4G|, (8)

[0087] After calculating the absolute difference D, the control chip can obtain the judgment threshold Δ for the current cycle based on the current communication mode (the communication mode before the target communication mode was determined and switched in the current cycle, i.e. the communication mode after the target communication mode was determined and switched in the previous cycle).

[0088] If the current communication mode is Bluetooth main mode or 4G main mode, obtain the set value (i.e., a fixed value set, denoted as S) as the judgment threshold Δ; if the current communication mode is Bluetooth 4G dual active mode, it is necessary to obtain the prediction threshold of the previous cycle (the calculation of the prediction threshold will be explained in detail later) as the judgment threshold Δ.

[0089] After determining the judgment threshold Δ for the current period, the control chip can make the following judgment:

[0090] If ScoreBLE > Score4G and D > Δ, it indicates that Bluetooth communication has a significant advantage in the current cycle, so the Bluetooth communication master mode is determined as the target communication mode.

[0091] If Score4G > ScoreBLE and D > Δ, it indicates that 4G communication has a significant advantage in the current cycle, so the 4G communication main mode is determined as the target communication mode.

[0092] If D≤Δ, then the control chip needs to predict the future trend of the absolute difference D to obtain the predicted difference D', and determine the target communication mode based on the predicted difference D'.

[0093] For example, the control chip can acquire historical cycle data, which includes Bluetooth signal quality parameters and 4G signal quality parameters from the n cycles prior to the current cycle. The relationship between historical cycles, the current cycle, and future cycles is as follows: Figure 3 As shown.

[0094] Accordingly, the control chip can determine the Bluetooth prediction data based on the Bluetooth signal quality parameters of the first n cycles, whereby the Bluetooth prediction data includes RSSI', PLR', and INT'.

[0095] RSSI', PLR', and INT' are calculated using the following formulas:

[0096] , (9)

[0097] , (10)

[0098] (11)

[0099] Where RSSI' is the predicted received signal strength for the future period, PLR' is the predicted packet loss rate for the future period, and INT' is the predicted interference intensity for the future period. This represents the average received signal strength over the previous n periods. Let be the received signal strength in the first i-th period out of the first n periods. for The corresponding weight (the larger i is, the higher the weight) The smaller the value, the better. Taking 10 historical periods as an example, the weights can be designed using a decay method of 1.0, 0.9, 0.8, ..., 0.1. Let be the packet loss rate of the first period out of the first n periods. and These are the packet loss rates for the first i-th period and the (i+1)-th period, respectively, out of the first n periods. for The corresponding weights (can be used with) (similar decay mechanism) This represents the average interference intensity over the first n periods. Let represent the interference intensity in the first i-th cycle out of the first n cycles. for The corresponding weights (using a decay mechanism). This represents the maximum difference in interference intensity between two adjacent periods in the first n periods. For reference interference intensity difference, If the difference in interference intensity between two adjacent periods in the first n periods exceeds the reference interference intensity difference. And the cycle number of the most recent historical cycle. This represents the average interference intensity over the first m cycles. Let be the interference intensity in the j-th cycle out of the first m cycles. for The corresponding weights (also using a decay mechanism).

[0100] Furthermore, the control chip can determine 4G prediction data based on the 4G signal quality parameters of the previous n cycles, whereby the 4G prediction data includes RSRP', SINR', and BSL'.

[0101] Since the parameter characteristics of RSRP', SINR', and BSL' are similar to those of RSSI', PLR', and INT', RSRP', SINR', and BSL' are calculated using the following formulas:

[0102] , (12)

[0103] (13)

[0104] (14)

[0105] Where RSRP' is the signal quality parameter for the predicted future period, SINR' is the signal-to-noise ratio for the predicted future period, and BSL' is the base station load for the predicted future period. This represents the mean of the signal quality parameters over the first n periods. Let be the signal quality parameter for the first i-th period out of the first n periods. for The corresponding weights This represents the average signal-to-noise ratio over the first n periods. Let be the signal-to-noise ratio of the first i-th cycle out of the first n cycles. for The corresponding weights This represents the maximum signal-to-noise ratio difference between two adjacent cycles in the first n cycles. For reference, the signal-to-noise ratio is poor. The signal-to-noise ratio difference between two adjacent cycles in the first n cycles exceeds the reference signal-to-noise ratio difference. And the cycle number of the most recent historical cycle. This represents the average signal-to-noise ratio over the first p periods. Let be the interference intensity in the k-th cycle out of the first m cycles. for The corresponding weights This represents the base station load in the first period out of the previous n periods. and These represent the base station load in the first i-th period and the base station load in the (i+1)-th period, respectively. for The corresponding weights.

[0106] After calculating the Bluetooth prediction data and the 4G prediction data, the prediction difference D' can be further determined.

[0107] For example, weighting coefficients w1', w2', and w3' need to be determined based on RSSI', PLR', and INT' in the Bluetooth prediction data, and the Bluetooth signal score ScoreBLE' for the future period needs to be calculated using the following formula:

[0108] ScoreBLE' = w1'*RSSI' + w2'*(1-PLR') + w3'*(1 / INT'), (15)

[0109] Furthermore, based on RSRP', SINR', and BSL' in the 4G prediction data, weighting coefficients k1', k2', and k3' are determined, and the 4G signal score Score4G' for the future period is calculated using the following formula:

[0110] Score4G' = k1'*RSRP'+ k2'*SINR'+ k3'*(1-BSL'), (16)

[0111] Calculate the predicted difference D' between the Bluetooth signal score ScoreBLE' and the 4G signal score Score4G' for the future period (as mentioned above, the absolute value of the difference between the two).

[0112] Based on this, the target communication mode can be determined based on the prediction difference D'.

[0113] In this embodiment, the Bluetooth 4G dual-active mode is further divided into Class I Bluetooth 4G dual-active mode, Class II Bluetooth 4G dual-active mode and Class III Bluetooth 4G dual-active mode.

[0114] Class I Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously. Data transmission adopts Class I hybrid transmission mode. In Class I hybrid transmission mode, when Bluetooth transmits data fragments, the data fragments transmitted by 4G are redundant at the end. If 4G completes the data fragment transmission, Bluetooth terminates the transmission of the redundant part. If 4G does not complete the data fragment transmission, Bluetooth continues to transmit the redundant part.

[0115] Class II Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously. Data transmission adopts Class II hybrid transmission mode. In Class II hybrid transmission mode, when 4G transmits data fragments, redundant data fragments are transmitted by Bluetooth at the end. If Bluetooth completes the data fragment transmission, 4G terminates the transmission of the redundant part. If Bluetooth does not complete the data fragment transmission, 4G continues the transmission of the redundant part.

[0116] Class III Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts Class III transmission mode. Class III transmission mode means that Bluetooth and 4G use data fragmentation transmission.

[0117] It should be noted that this embodiment does not consider applications in high-reliability scenarios (such as medical devices, industrial scenarios, etc.), because in such scenarios, dual-mode simultaneous activation is usually maintained for a long time to ensure reliable data transmission, and the switching problem mainly addressed in this embodiment is almost irrelevant. For example, Bluetooth transmission of a data segment takes 100ms, and once the transmission of the redundant data segment is completed, the transmission of the redundant part is terminated. This process can be controlled within 50ms, thus saving bandwidth and improving efficiency in such cases.

[0118] Accordingly, the control chip can determine the prediction threshold Δ' based on RSSI', PLR', and INT' from the Bluetooth prediction data and RSRP', SINR', and BSL' from the 4G prediction data. The prediction threshold Δ' can be determined dynamically by considering the changes in each prediction parameter value relative to the current period, or it can be an empirical value set based on the type of IoT device and the application scenario, for example:

[0119] Δ'=0.5S+0.5*[a1*(RSSI'- RSSI)+ a2*( PLR'- PLR) + a3*( INT'- INT) +a4*( RSRP'- RSRP) + a5*( SINR'- SINR) + a6*( BSL'- BSL), (17)

[0120] Where S is a fixed value, and a1, a2, a3, a4, a5, and a6 are weight parameters.

[0121] Once the prediction threshold Δ' is determined, it can be judged that:

[0122] If ScoreBLE'>Score4G' and D'>Δ', then the Class I Bluetooth 4G dual-active mode is determined as the target communication mode.

[0123] If Score4G'>ScoreBLE' and D'>Δ', then the Class II Bluetooth 4G dual-active mode is determined as the target communication mode.

[0124] If D'≤Δ', then the Class III Bluetooth 4G dual-active mode is determined as the target communication mode.

[0125] Based on RSSI', PLR', and INT' from Bluetooth prediction data and RSRP', SINR', and BSL' from 4G prediction data, a prediction threshold Δ' is determined. If ScoreBLE' > Score4G' and D' > Δ', the Class I Bluetooth 4G dual-active mode is determined as the target communication mode. In Class I Bluetooth 4G dual-active mode, Bluetooth and 4G operate simultaneously, and data transmission uses a Class I hybrid transmission mode. In this mode, when Bluetooth transmits data fragments, 4G data fragments are redundantly transmitted at the end. If 4G completes data fragment transmission, Bluetooth transmission of the redundant portion is terminated; if 4G does not complete data fragment transmission, Bluetooth transmission of the redundant portion continues. If Score4G' > ScoreBLE' and D' > Δ', the Class II Bluetooth 4G dual-active mode is determined as the target communication mode. In this context, Class II Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts Class II hybrid transmission mode. In Class II hybrid transmission mode, when 4G transmits data fragments, redundant data fragments are transmitted by Bluetooth at the end. If Bluetooth completes the data fragment transmission, 4G terminates the transmission of the redundant part; if Bluetooth does not complete the data fragment transmission, 4G continues the transmission of the redundant part. If D'≤Δ', Class III Bluetooth 4G dual-active mode is determined as the target communication mode. In this context, Class III Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts Class III transmission mode. Class III transmission mode means that Bluetooth and 4G use data fragment transmission.

[0126] Once the target communication mode is determined, the control chip can control the switching of communication modes between the two communication modules based on that mode. For example, it can switch from the Bluetooth primary communication mode to a Category III Bluetooth 4G dual-active mode; or it can switch from a Category II Bluetooth 4G dual-active mode to the 4G primary communication mode. This switching mechanism allows the dual-active mode to serve as a good transition between modes and fully leverage its advantages. For example, when a user device gradually approaches an IoT device from a distance, it will switch from the primary 4G communication mode to the dual-active mode. Similarly, at close range, after the dual-active mode has been working normally for a period, the presence of movement or complex interference sources may cause the 4G (or Bluetooth) communication to deteriorate, making Bluetooth (or 4G) the dominant communication mode. Due to the predictive mechanism of the dual-active mode, this trend can be detected in advance, and appropriate countermeasures for different dual-active modes can be prepared accordingly (e.g., when Bluetooth becomes dominant, it will transition to a Class I Bluetooth 4G dual-active mode, at which point redundancy has already been implemented. In the next cycle or after two or three cycles, it needs to switch from the Class I Bluetooth 4G dual-active mode to the Bluetooth primary communication mode. Since redundancy has already been implemented during transmission, 4G can directly exit the data transmission task), thus achieving a "seamless switchover."

[0127] Overall, the Bluetooth 4G dual-mode communication method adopted in this embodiment effectively avoids common existing hard handover schemes (which are prone to brief interruptions, with typical latency >500ms), while the handover latency can be controlled within 100ms (in most cases, the risk of communication interruption can be avoided, and the handover response time can be shortened to within 50ms). Handover accuracy is also effectively improved. Due to the comprehensive calculation and scoring of multi-dimensional parameters, misjudgments caused by parameter bias are eliminated, resulting in a handover decision accuracy improvement of over 25% (compared to existing single-parameter hard handover schemes). In dual-active mode, three different dual-active modes with different focuses are designed to achieve effective collaboration between Bluetooth and 4G, thereby improving throughput while reducing power consumption. Furthermore, packet loss caused by communication interruption during handover is effectively suppressed, achieving near-perfect "seamless handover".

[0128] Some extended solutions based on this solution (extended solutions that add some technical features to this solution) should fall within the scope of protection of this patent; and some alternative solutions to this solution: for example, using signal strength (Bluetooth RSSI and 4G RSRP), Bluetooth packet loss rate and 4G base station load to calculate the score, although it is expected to reduce the accuracy of handover decision by about 15%-20%, it is still better than the traditional single-parameter solution; or, when acquiring parameters, using hardware acquisition methods or software to simulate signal monitoring (compared to hardware acquisition methods, it is expected that the main control load will increase, power consumption will increase, the real-time performance of score calculation will decrease, and the single cycle duration of the overall handover solution's operation cycle will also need to be increased), should also be considered equivalent solutions to this solution and fall within the scope of protection of this patent.

[0129] In summary, this application provides a Bluetooth / 4G dual-mode communication method for IoT devices. The IoT device includes a control chip and a dual-communication module encompassing both Bluetooth and 4G communication. The Bluetooth / 4G dual-mode communication method provided in this solution is applied to the control chip. It acquires Bluetooth signal quality parameters (including RSSI, PLR, and INT) and 4G signal quality parameters (including RSRP, SINR, and BSL). Based on the Bluetooth signal quality parameters, it calculates a Bluetooth signal score (ScoreBLE), and based on the 4G signal quality parameters, it calculates a 4G signal score (Score4G). Based on this, it determines the target communication mode (Bluetooth primary mode, 4G primary mode, and Bluetooth / 4G dual-active mode. Bluetooth primary mode indicates that Bluetooth is the current primary link and 4G is in low-power standby mode; 4G primary mode indicates that 4G is the current primary link and Bluetooth maintains a heartbeat connection; Bluetooth / 4G dual-active mode indicates that Bluetooth and 4G work simultaneously). This solution sets three modes and designs an intelligent scoring mechanism. If a device has a significant advantage in a certain communication mode (e.g., Bluetooth or 4G) (its score is significantly higher than the other), it uses single-mode communication to reduce power consumption. Furthermore, using Bluetooth 4G dual-active mode as a transition for mode switching facilitates smooth switching. In situations where the scores for Bluetooth and 4G communication are not significantly different (e.g., both scores are high or low), Bluetooth 4G dual-active mode establishes a collaborative mechanism when both modes are active simultaneously, effectively leveraging the advantages of dual-mode communication and reducing bandwidth redundancy.

[0130] In Bluetooth 4G dual-active mode, a complete prediction, coordination, and switching mechanism is established, which can effectively leverage its smooth transition function. When a communication mode switch may occur in the predicted future period, partial redundancy is implemented in advance. This redundancy is dynamic and can make intelligent decisions based on the data transmission status of the redundant data (when the redundant party completes the data fragment transmission normally, the transmission of the redundant data fragment at the end is terminated in time, which can effectively save bandwidth redundancy; when the redundant party fails to complete the data fragment transmission normally, the transmission of the redundant data fragment at the end continues, which can ensure data integrity and effectively reduce data retransmission). This ensures reliable data transmission and saves bandwidth when unnecessary, achieving efficient coordination when Bluetooth 4G dual-mode communication is activated simultaneously, effectively leveraging the advantages of dual-mode communication.

[0131] In Bluetooth 4G dual-active mode, a sophisticated handover mechanism is designed. This mechanism predicts and subdivides Bluetooth 4G dual-active modes into three distinct modes to handle different predicted scenarios. By anticipating upcoming handovers and adapting to them using the corresponding data transmission mechanisms, a near-seamless handover can be achieved when the next cycle arrives. This avoids the unsmooth handover issues caused by existing dual-mode communication handover mechanisms (hard handovers cause brief communication interruptions, with typical latency >500ms, resulting in data packet loss or service disruption). In the prediction, based on Bluetooth signal quality parameters (including RSSI, PLR, INT) and 4G signal quality parameters (including RSRP, SINR, BSL) in complex scenarios (such as common mobile scenarios, which often cause significant fluctuations in one or more parameters), a parameter prediction scheme tailored to their characteristics was designed. (For different parameter characteristics, corresponding parameter prediction formulas were designed, and corresponding prediction mechanisms were specifically designed based on the possible abrupt changes of various parameters in mobile scenarios. Appropriate weights were assigned according to the proximity of historical periods, enabling the prediction accuracy to reach over 80%. The accuracy of the prediction significantly reduces the additional burden caused by redundancy in several types of Bluetooth 4G dual-active modes, while the overall computational load is not large and does not take much time.) Furthermore, in this case, a dynamic threshold adjustment mechanism was designed to handle special cases specially, making the switching mechanism more intelligent and helping to significantly reduce or even avoid the occurrence of the "ping-pong effect."

[0132] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A Bluetooth 4G dual-mode communication method for an Internet of Things (IoT) device, characterized in that, The IoT device is equipped with a control chip and a dual communication module including Bluetooth and 4G communication. The method is applied to the control chip and includes: Obtain Bluetooth signal quality parameters and 4G signal quality parameters. The Bluetooth signal quality parameters include RSSI, PLR, and INT. RSSI is the received signal strength, PLR is the packet loss rate, and INT is the interference strength. The 4G signal quality parameters include RSRP, SINR, and BSL. RSRP is the reference signal received power, SINR is the signal-to-noise ratio, and BSL is the base station load. Bluetooth signal score ScoreBLE is calculated based on Bluetooth signal quality parameters, and 4G signal score Score4G is calculated based on 4G signal quality parameters. Based on the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, the target communication mode is determined, and the communication mode switching of the dual communication modules is controlled based on the target communication mode. The communication modes include Bluetooth main communication mode, 4G main communication mode, and Bluetooth 4G dual-active mode. Bluetooth main communication mode means that Bluetooth is the current main link and 4G is in low power standby mode. 4G main communication mode means that 4G is the current main link and Bluetooth maintains a heartbeat connection. Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously. The Bluetooth / 4G dual-mode communication method for IoT devices operates in two modes: periodic operation and signal event-driven triggering. Based on Bluetooth signal score ScoreBLE and 4G signal score Score4G, the target communication mode is determined, including: Calculate the absolute difference D between the Bluetooth signal score ScoreBLE and the 4G signal score Score4G; Based on the current communication mode, obtain the judgment threshold Δ for the current period; If ScoreBLE > Score4G and D > Δ, then the Bluetooth communication master mode is determined to be the target communication mode. If Score4G > ScoreBLE and D > Δ, then the 4G communication master mode is determined to be the target communication mode. If D≤Δ, predict the future trend of the absolute difference D to obtain the predicted difference D', and determine the target communication mode based on the predicted difference D'; To predict the future trend of the absolute difference D, we obtain the predicted difference D', including: Acquire historical period data, which includes Bluetooth signal quality parameters and 4G signal quality parameters for the n periods preceding the current period; Based on the Bluetooth signal quality parameters of the first n cycles, Bluetooth prediction data is determined, which includes RSSI', PLR', and INT'. Based on the 4G signal quality parameters of the first n cycles, the 4G prediction data is determined, which includes RSRP', SINR', and BSL'. The prediction difference D' is determined based on Bluetooth prediction data and 4G prediction data.

2. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 1, characterized in that, The calculation of Bluetooth signal score ScoreBLE based on Bluetooth signal quality parameters, and the calculation of 4G signal score Score4G based on 4G signal quality parameters, include: Before calculating the Bluetooth signal score ScoreBLE and the 4G signal score Score4G, the parameters are normalized by mapping the original values ​​of RSSI, PLR, INT, RSRP, SINR, and BSL to the [0,1] interval. The Bluetooth signal score ScoreBLE is calculated using the following formula: ScoreBLE = w1*RSSI + w2*(1-PLR) + w3*(1 / INT), Wherein, ScoreBLE is the Bluetooth signal score, and w1, w2, and w3 are the weighting coefficients for the Bluetooth signal quality parameters; The 4G signal score, Score4G, is calculated using the following formula: Score4G = k1*RSRP + k2*SINR + k3*(1-BSL), Among them, Score4G is the 4G signal score, and k1, k2 and k3 are the weighting coefficients of the 4G signal quality parameters.

3. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 1, characterized in that, Based on the Bluetooth signal quality parameters of the first n cycles, Bluetooth prediction data is determined, including: RSSI', PLR', and INT' are calculated using the following formulas: ， ， ， Where RSSI' is the predicted received signal strength for the future period, PLR' is the predicted packet loss rate for the future period, and INT' is the predicted interference intensity for the future period. This represents the average received signal strength over the previous n periods. Let be the received signal strength in the first i-th period out of the first n periods. for The corresponding weights Let be the packet loss rate of the first period out of the first n periods. and These are the packet loss rates for the first i-th period and the (i+1)-th period, respectively, out of the first n periods. for The corresponding weights This represents the average interference intensity over the first n periods. Let represent the interference intensity in the first i-th cycle out of the first n cycles. for The corresponding weights This represents the maximum difference in interference intensity between two adjacent periods in the first n periods. For reference interference intensity difference, If the difference in interference intensity between two adjacent periods in the first n periods exceeds the reference interference intensity difference. And the cycle number of the most recent historical cycle. This represents the average interference intensity over the first m cycles. Let be the interference intensity in the j-th cycle out of the first m cycles. for The corresponding weights.

4. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 1, characterized in that, Based on the 4G signal quality parameters of the first n cycles, the 4G prediction data is determined, including: RSRP', SINR', and BSL' are calculated using the following formulas: ， ， ， Where RSRP' is the signal quality parameter for the predicted future period, SINR' is the signal-to-noise ratio for the predicted future period, and BSL' is the base station load for the predicted future period. This represents the mean of the signal quality parameters over the first n periods. Let be the signal quality parameter for the first i-th period out of the first n periods. for The corresponding weights This represents the average signal-to-noise ratio over the first n periods. Let be the signal-to-noise ratio of the first i-th cycle out of the first n cycles. for The corresponding weights This represents the maximum signal-to-noise ratio difference between two adjacent cycles in the first n cycles. For reference, the signal-to-noise ratio is poor. The signal-to-noise ratio difference between two adjacent cycles in the first n cycles exceeds the reference signal-to-noise ratio difference. And the cycle number of the most recent historical cycle. This represents the average signal-to-noise ratio over the first p periods. Let be the interference intensity in the k-th cycle out of the first m cycles. for The corresponding weights This represents the base station load in the first period out of the previous n periods. and These represent the base station load in the first i-th period and the base station load in the (i+1)-th period, respectively. for The corresponding weights.

5. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 1, characterized in that, The prediction difference D' is determined based on Bluetooth prediction data and 4G prediction data, including: Based on RSSI', PLR', and INT' in the Bluetooth prediction data, weighting coefficients w1', w2', and w3' are determined, and the Bluetooth signal score ScoreBLE' for future periods is calculated using the following formula: ScoreBLE' = w1'*RSSI' + w2'*(1-PLR') + w3'*(1 / INT'), ScoreBLE' is the Bluetooth signal score for the future cycle; Based on RSRP', SINR', and BSL' in the 4G prediction data, weighting coefficients k1', k2', and k3' are determined, and the 4G signal score Score4G' for the future period is calculated using the following formula: Score4G' = k1'*RSRP'+ k2'*SINR'+ k3'*(1-BSL'), Among them, Score4G' is the 4G signal score for the future period; Calculate the predicted difference D' between the Bluetooth signal score ScoreBLE' for the future period and the 4G signal score Score4G' for the future period.

6. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 1, characterized in that, Bluetooth 4G dual-active modes include Class I, Class II, and Class III Bluetooth 4G dual-active modes. The target communication mode is determined based on the prediction difference D', including: Based on RSSI', PLR', and INT' in the Bluetooth prediction data and RSRP', SINR', and BSL' in the 4G prediction data, the prediction threshold Δ' is determined. If ScoreBLE'>Score4G' and D'>Δ', then the Class I Bluetooth 4G dual-active mode is determined as the target communication mode. In Class I Bluetooth 4G dual-active mode, Bluetooth and 4G work simultaneously, and data transmission adopts Class I hybrid transmission mode. In Class I hybrid transmission mode, when Bluetooth transmits data fragments, the data fragments transmitted by 4G are redundant at the end. If 4G completes the data fragment transmission, Bluetooth terminates the transmission of the redundant part. If 4G does not complete the data fragment transmission, Bluetooth continues the transmission of the redundant part. If Score4G'>ScoreBLE' and D'>Δ', then the Class II Bluetooth 4G dual-active mode is determined as the target communication mode. In Class II Bluetooth 4G dual-active mode, Bluetooth and 4G work simultaneously, and data transmission adopts Class II hybrid transmission mode. In Class II hybrid transmission mode, when 4G transmits data fragments, redundant data fragments are transmitted by Bluetooth at the end. If Bluetooth completes the data fragment transmission, 4G terminates the transmission of the redundant part. If Bluetooth does not complete the data fragment transmission, 4G continues the transmission of the redundant part. If D'≤Δ', then the Class III Bluetooth 4G dual-active mode is determined as the target communication mode. In this mode, the Class III Bluetooth 4G dual-active mode means that Bluetooth and 4G work simultaneously, and data transmission adopts the Class III transmission mode. The Class III transmission mode means that Bluetooth and 4G use data fragmentation transmission.

7. The Bluetooth 4G dual-mode communication method for IoT devices according to claim 6, characterized in that, Based on the current communication mode, obtain the judgment threshold Δ for the current period, including: If the current communication mode is Bluetooth main mode or 4G main mode, obtain the set value as the judgment threshold Δ; If the current communication mode is Bluetooth 4G dual-active mode, the prediction threshold of the previous cycle is obtained as the judgment threshold Δ.

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