A short-range intercom channel adaptive tuning system

Abstract

The application discloses a short-distance intercom channel adaptive tuning system, and relates to the technical field of close-range communication, which comprises the following steps: in response to a communication request initiated by a source intercom, collecting a set of characteristic parameters in a current short-distance environment, calculating initial scene adaptation scores of each service channel, and correcting the initial scene adaptation score weights based on the mapping relationship between each characteristic parameter and the actual communication quality index within a historical communication period; determining the channel with the highest corrected scene adaptation score as a target communication channel; updating the set of characteristic parameters at a preset interval and calculating the scene adaptation score of the current channel during communication maintenance; and triggering power lifting and modulation order reduction when the scene adaptation score is lower than a preset threshold. The application solves the technical problem of low resource scheduling efficiency caused by poor communication link stability and weak anti-interference ability of the existing intercom channel system in a complex mobile scene.

Description

Technical Field

[0001] This application relates to the field of short-range communication technology, specifically to a short-range walkie-talkie channel adaptive tuning system. Background Technology

[0002] Short-range walkie-talkies are increasingly used in emergency command, security patrols, and industrial collaboration. However, these scenarios typically involve highly dynamic propagation environments, such as multipath effects caused by personnel movement, signal fading due to human obstruction, sudden electromagnetic interference, and the non-stationary characteristics of background noise within the speech frequency band. All of these factors can affect the stability of the communication link and voice intelligibility. Traditional short-range walkie-talkie systems often employ fixed channel allocation or simple signal strength-based switching mechanisms, which are insufficient to effectively handle the aforementioned complex and time-varying channel conditions.

[0003] In existing technologies, some solutions attempt to introduce environmental awareness mechanisms, such as channel quality assessment based on received signal strength indicators or signal-to-noise ratio, and select communication channels accordingly. However, such methods typically rely on only a single or static parameter and do not fully consider the coupled impact of human motion and noise spectrum characteristics on channel availability. Especially in walking scenarios, arm swinging movements periodically block the antenna and cause signal fluctuations. If background noise exhibits a spike distribution within the speech frequency band, such as alarm sounds or mechanical roars, it will cause non-uniform interference to speech coding. If a scoring model with uniform weights is still used, it is very easy to lead to channel selection bias, which in turn can cause an increase in the bit error rate or link interruption.

[0004] Furthermore, existing systems generally lack effective utilization of historical communication data and fail to establish a dynamic mapping relationship between characteristic parameters and actual communication quality. Therefore, they cannot adaptively adjust evaluation strategies when facing similar environments, and it is also difficult to achieve accurate prediction of channel performance, resulting in intermittent communication experience and lagging resource scheduling. Summary of the Invention

[0005] This application provides a short-range walkie-talkie channel adaptive tuning system, which solves the technical problems of poor communication link stability and weak anti-interference capability in existing walkie-talkie channel systems under complex mobile scenarios, resulting in low resource scheduling efficiency.

[0006] The technical solution to the above-mentioned technical problems in this application is as follows: This application provides a short-range walkie-talkie channel adaptive tuning system, including: The feature parameter acquisition module is used to respond to the communication request initiated by the source walkie-talkie and acquire the feature parameter set in the current short-range environment. The feature parameter set includes the human walking arm swing period, the sliding window variance of the received signal strength indication value, and the distribution flatness of the background noise power spectrum in the speech band. The initial score calculation module is used to calculate the initial scenario adaptation score of each service channel based on the feature parameter set, wherein the weight coefficient of the distribution flatness is positively correlated with the human walking arm swing period, and the weight coefficient of the sliding window variance is negatively correlated with the human walking arm swing period. The communication channel selection module is used to extract sensitivity coefficients and cross-coupling coefficients based on the mapping relationship between various characteristic parameters and actual communication quality indicators during historical communication periods, and to correct the initial scene adaptation score weights based on the sensitivity coefficients and cross-coupling coefficients. The channel with the highest scene adaptation score after correction is determined as the target communication channel, and radio frequency tuning parameters are generated to establish a communication link. The link maintenance module is used to update the feature parameter set at preset intervals and calculate the scenario adaptation score of the current channel during communication maintenance. When the scenario adaptation score is lower than a preset threshold, it triggers power boost and modulation order downgrade.

[0007] This application provides one or more technical solutions, which have at least the following technical effects or advantages: This application provides a short-range walkie-talkie channel adaptive tuning system. First, it comprehensively characterizes the dynamic changes in the short-range communication environment by introducing multi-dimensional feature parameters such as the human walking arm swing period, the sliding window variance of the received signal strength indication value, and the flatness of the background noise power spectrum distribution within the speech band. Second, it constructs a multiple linear regression model using historical communication data, extracting sensitivity coefficients and cross-coupling coefficients to dynamically correct the initial scoring weights, thus realizing the transformation from static rules to a dynamic adaptive strategy. Finally, by monitoring the scene adaptation score in real time during communication maintenance and combining it with graded response mechanisms such as power boosting, modulation order reduction, and channel renegotiation, a closed-loop link maintenance strategy is formed. This not only ensures communication continuity when channel quality is slightly degraded but also allows for rapid switching to a better channel when severely degraded, significantly reducing the drop rate and bit error rate, improving the utilization efficiency of spectrum resources, and enhancing the overall robustness of the communication system.

[0008] Through the above technical solution, this application quantifies the impact of human arm swinging motion on signal reception and performs a weighted evaluation based on the spectral characteristics of background noise. This effectively avoids misjudgments caused by fluctuations in a single signal strength index, improving the accuracy and stability of channel selection. Simultaneously, the hierarchical link maintenance and renegotiation strategy minimizes unnecessary channel switching and power consumption while ensuring communication quality, extending the battery life of terminal devices. This makes it suitable for practical applications with extremely high communication reliability requirements and complex environments, such as emergency command and security for large-scale events. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the structure of a short-range walkie-talkie channel adaptive tuning system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the process for calculating the initial scene adaptation score in a short-range walkie-talkie channel adaptive tuning system provided in an embodiment of this application.

[0011] The components represented by each number in the attached diagram are explained below: Feature parameter acquisition module 11, initial score calculation module 12, communication channel optimization module 13, link maintenance module 14. Detailed Implementation

[0012] This application provides a short-range walkie-talkie channel adaptive tuning system to address the technical problems of poor communication link stability and weak anti-interference capability in existing walkie-talkie channel systems under complex mobile scenarios, resulting in low resource scheduling efficiency.

[0013] Examples, such as Figure 1 As shown in the figure, this application provides a short-range walkie-talkie channel adaptive tuning system, including: The feature parameter acquisition module 11 is used to respond to the communication request initiated by the source walkie-talkie and acquire the feature parameter set in the current short-range environment. The feature parameter set includes the human walking arm swing period, the sliding window variance of the received signal strength indication value, and the distribution flatness of the background noise power spectrum in the speech band. In this embodiment of the application, when the user of the source walkie-talkie presses a button to initiate a communication request, the perception and measurement of the current short-range wireless environment is immediately started. Through the triaxial accelerometer, the received signal strength indication detection module and the background noise spectrum analysis module integrated in the source walkie-talkie, the human walking arm swing period, the sliding window variance of the received signal strength indication value and the distribution flatness of the background noise power spectrum in the speech band are collected simultaneously to form a set of characteristic parameters in the current short-range environment.

[0014] Specifically, the arm swing cycle of human walking is obtained by acquiring the time period of the user's arm swing through an accelerometer. For example, the peak interval of the accelerometer is recorded within a 0.5-second time window, and the average value is taken to obtain the arm swing cycle. For example, the arm swing cycle of human walking in a stationary state is 0 seconds; when walking slowly, the arm swing cycle of human walking is about 1.2 seconds; when walking at a medium speed, the arm swing cycle of human walking is about 0.9 seconds; when walking fast or running, the arm swing cycle of human walking is about 0.6 seconds. The sliding step size of the acquisition window for this parameter is 0.1 seconds to ensure real-time performance.

[0015] The variance of the sliding window of the received signal strength indicator is calculated by continuously collecting multiple sample points in the last second, for example, 20 sample points. The larger the variance value, the more severe the influence of the human shadow effect or multipath fading on the channel.

[0016] The flatness of the background noise power spectrum distribution within the speech band is determined by performing a Fast Fourier Transform on the speech band from 300Hz to 3400Hz, dividing the band into eight equal-width sub-bands, each approximately 387.5Hz. The ratio of the root mean square error to the mean of the noise power in each sub-band is calculated as the flatness index. The closer the flatness value is to 1, the more likely the noise is white noise. The greater the flatness value deviates from 1, such as being concentrated in a narrow band, the more likely strong narrowband interference exists. For example, the flatness of the distribution in a typical urban environment is between 0.7 and 0.9, while it can be as low as 0.3 when there is co-channel interference.

[0017] The initial score calculation module 12 is used to calculate the initial scenario adaptation score of each service channel according to the feature parameter set, wherein the weight coefficient of the distribution flatness is positively correlated with the human walking arm swing period, and the weight coefficient of the sliding window variance is negatively correlated with the human walking arm swing period. In the embodiments of this application, such as Figure 2 As shown, the arm swing cycle of human walking is first divided into swing intervals with different speeds to reflect the expected degree of signal fading under different motion states. Then, the sliding window variance of the received signal strength indicator value is normalized into a channel fluctuation factor, which ranges from 0 to 1, with a larger value indicating more severe signal fluctuation. Simultaneously, the flatness of the background noise power spectrum distribution within the speech band is converted into sub-band interference coefficients. Lower flatness indicates the presence of a specific narrowband interference source, while higher sub-band interference coefficients result in a greater penalty weight for that channel.

[0018] Finally, based on the swing arm cycle weight benchmark value, the reliability coefficient and sub-band interference coefficient are weighted and summed to obtain the initial scenario adaptation score of each service channel. This score reflects the theoretical availability of each channel under the current motion state and noise environment.

[0019] Specifically, the initial scenario adaptation score for each service channel is calculated based on the set of feature parameters, including: The human walking arm swing cycle is divided into a low-speed swing interval, a medium-speed swing interval, and a high-speed swing interval, and a weighted benchmark value of the arm swing cycle is preset for each interval. The variance of the sliding window is normalized to a channel fluctuation factor, and the reliability coefficient of the received signal strength indication value is dynamically adjusted according to the channel fluctuation factor. The flatness of the background noise power spectrum distribution within the speech band is converted into subband interference coefficients; Based on the swing arm period weight benchmark value, the credibility coefficient and the sub-band interference coefficient are weighted and summed to obtain the initial scenario adaptation score for each service channel.

[0020] In this embodiment, the arm swing period of human walking is divided into three intervals, and a weight benchmark value of the arm swing period is preset for each interval. The shorter the arm swing period, the faster the movement and the more frequent the changes in signal blockage by the human body. At this time, it should rely more on the channel characteristics that resist fast fading. Therefore, the weight of the distribution flatness should be increased.

[0021] For example, in the low-speed swing range, the arm swing period of human walking is greater than 1.0 second, corresponding to standing still or walking very slowly. The preset weight benchmark value of the arm swing period is 0.4. For example, when the arm swing period is 1.2 seconds, the weight benchmark value is 0.4.

[0022] The arm swing cycle of human walking in the medium-speed swing range is between 0.7 seconds and 1.0 seconds, which corresponds to normal walking. The preset weight benchmark value of the arm swing cycle is 0.6. For example, when the arm swing cycle is 0.9 seconds, the weight benchmark value is 0.6.

[0023] In the high-speed swing range, the human arm swing cycle is less than 0.7 seconds, corresponding to fast walking or running. The preset weight benchmark value for the arm swing cycle is 0.85. For example, when the arm swing cycle is 0.6 seconds, the weight benchmark value is 0.85.

[0024] Secondly, the sliding window variance is normalized to the channel fluctuation factor, i.e., channel fluctuation factor = min(sliding window variance / 50, 1.0), where 50dB² is an empirical saturation value derived from the upper limit of variance in the indoor corridor environment in historical measurements. Then, the reliability coefficient of the received signal strength indication value is dynamically adjusted. For example, it can be calculated by the formula reliability coefficient = 1 - 0.6 × channel fluctuation factor. When the channel fluctuation factor = 0, indicating no fluctuation, the reliability coefficient = 1.0; when the channel fluctuation factor = 1.0, indicating severe fluctuation, the reliability coefficient = 0.4, where 0.6 is the fluctuation suppression coefficient, which is used to characterize the penalty for the reliability of the received signal strength indication value when the signal fluctuates severely. In this embodiment, the value is taken as 0.6 because, based on the fitting of measured data, while ensuring the reference value of signal strength, it effectively suppresses the misleading effect of instantaneous signal drop caused by human body obstruction on the scoring. This value can be fine-tuned according to specific circumstances. For example, in an open outdoor environment, due to the weak multipath effect, the fluctuation suppression coefficient can be adjusted to 0.4 to retain more signal strength information.

[0025] Next, the background noise distribution flatness is converted into subband interference coefficient. Specifically, the subband interference coefficient = 1 / (background noise distribution flatness + 0.1), where +0.1 is to prevent division by zero and adjust the sensitivity. When the background noise distribution flatness = 1.0, indicating ideal flatness, the subband interference coefficient ≈ 0.91; when the background noise distribution flatness = 0.3, indicating severe narrowband interference, the subband interference coefficient = 2.5. However, to prevent excessive weighting, the subband interference coefficient is limited to the range of [0.8, 2.5]. For example, if the measured background noise distribution flatness = 0.7, then the subband interference coefficient = 1 / (0.7 + 0.1) = 1.25.

[0026] Furthermore, the initial scenario adaptation score is obtained by weighted summation. For each service channel i, the initial scenario adaptation score = arm swing period weight benchmark value × δi + (1 - arm swing period weight benchmark value) × γi, where δi and γi are the sub-band interference coefficient and the confidence coefficient of the received signal strength indication value measured under this channel, respectively. The meaning of this formula is that the faster the arm swing, that is, the larger the preset arm swing period weight benchmark value, the more attention is paid to noise flatness, that is, anti-interference capability; the slower the arm swing, the more attention is paid to the stability of the received signal strength indication value.

[0027] For example, assuming the current swing arm period is 0.9 seconds, which is in the medium-speed swing range, the swing arm period weight benchmark value is 0.6, the subband interference coefficient measured by channel 1 is 1.25, and the confidence coefficient of the received signal strength indication value is 0.7, then the initial scene adaptation score of channel 1 is 0.6×1.25+0.4×0.7=0.75+0.28=1.03. If the δ=0.95 and γ=0.9 measured by channel 2, then the initial scene adaptation score of channel 2 is 0.6×0.95+0.4×0.9=0.57+0.36=0.93. At this time, the initial score of channel 1 is higher, indicating that under the current motion state, although the signal strength of channel 1 fluctuates slightly more, its background noise environment is relatively pure and its anti-narrowband interference capability is stronger. Therefore, it is judged as a better candidate channel.

[0028] The communication channel selection module 13 is used to extract the sensitivity coefficient and cross-coupling coefficient based on the mapping relationship between each characteristic parameter and the actual communication quality index during the historical communication period, and to correct the initial scene adaptation score weight based on the sensitivity coefficient and the cross-coupling coefficient, and to determine the channel with the highest scene adaptation score after correction as the target communication channel, and to generate radio frequency tuning parameters to establish a communication link. In this embodiment of the application, a multiple linear regression model is constructed using historical communication logs stored in a local database or cloud server. The model uses the set of feature parameters within a historical period as independent variables and the actual communication quality index as dependent variables. It extracts the sensitivity coefficient of the impact of each feature parameter on communication quality, as well as the cross-coupling coefficient between feature parameters. The sensitivity coefficient reflects the degree of contribution of a single feature parameter change to the communication quality index, and the cross-coupling coefficient reflects the mutual influence relationship between different feature parameters.

[0029] Specifically, based on the mapping relationship between various characteristic parameters and actual communication quality indicators within historical communication periods, sensitivity coefficients and cross-coupling coefficients are extracted, including: Obtain the feature parameter sequences and corresponding actual communication quality indicators for multiple recent historical communication periods. The actual communication quality indicators include at least bit error rate, signal-to-noise ratio, and voice intelligibility. A multiple linear regression model is constructed using the feature parameter sequence as input and the actual communication quality index as output. The independent influence factors of each feature parameter on communication quality are extracted from the multiple linear regression model as the sensitivity coefficient, and the interaction influence factors between different feature parameters are extracted as the cross-coupling coefficient.

[0030] In this embodiment of the application, the feature parameter sequence and corresponding actual communication quality index of the most recent historical communication periods, such as the most recent 50 historical communication periods, are first obtained to construct a training dataset. The feature parameter sequence includes the normalized swing arm period, channel fluctuation factor and subband interference coefficient. The actual communication quality index selects the reciprocal of the bit error rate as the main evaluation basis, supplemented by the signal-to-noise ratio and voice clarity score.

[0031] Secondly, using the feature parameter sequence as input and the actual communication quality index as output, a multiple linear regression model is constructed. The mathematical expression of this model can be represented as Y=β0+β1X1+β2X2+β3X3+β 12 X1X2+β13X1X3+β 23 X2X3+ε, where Y is the predicted communication quality score, X1, X2, and X3 represent the weighted terms related to the swing arm period, the channel fluctuation factor, and the subband interference coefficient, respectively, and β1, β2, and β3 are the independent influencing factors of each feature, β 12 β 13 β 23 ε is the cross-coupling coefficient, and ε is the residual term. The model parameters are fitted using the least squares method.

[0032] Secondly, the independent influence factors of each feature parameter on communication quality are extracted from the multiple linear regression model as sensitivity coefficients, and the interaction influence factors between different feature parameters are extracted as cross-coupling coefficients. Specifically, the sensitivity coefficient reflects the degree of direct impact of changes in motion state on communication quality when other conditions remain unchanged; the cross-coupling coefficient reflects the nonlinear superposition effect between signal fluctuations and noise interference under high dynamic motion.

[0033] Furthermore, based on the sensitivity coefficient and the cross-coupling coefficient, the initial scene adaptation score weights are corrected, including: The sensitivity coefficient is used as a first-order correction factor for each feature parameter to adjust the weight of the corresponding feature parameter in the initial scene adaptation score. The cross-coupling coefficient is used as a second-order compensation factor and is multiplied and superimposed on the associated feature parameter weights to form a weight correction matrix. The corrected scene adaptation score is obtained based on the weight correction matrix.

[0034] In this embodiment, the sensitivity coefficient is first applied directly to the corresponding term in the initial scoring calculation formula. For example, if the model extracts the sensitivity coefficient of the arm swing period to the communication quality as β1, then the baseline value of the arm swing period weight in the original formula is adjusted to the baseline value + (λ1β1), where λ1 is the learning rate, which can be set to 0.2. The adjusted arm swing period weight is limited to [0.3, 0.9] because an excessively high weight will lead to oversensitivity to the motion state and ignore the strength advantage of the signal itself, while an excessively low weight cannot effectively compensate for the impact of fast fading. The limiting operation ensures the robustness of the weight adjustment, and the weight is truncated if it exceeds the range.

[0035] Secondly, the cross-coupling coefficients are applied to the interaction terms between feature parameters. For example, the cross-coupling coefficients are multiplied and added to the weights of the associated feature parameters to form a 3×3 weight correction matrix K, where K xy =1 + normalized β xy .

[0036] Through the first-order amplitude adjustment and second-order interactive compensation described above, the final weight correction matrix is ​​generated, and this matrix is ​​applied to the initial scenario adaptation score of each service channel to obtain the corrected scenario adaptation score.

[0037] For example, if the initial scenario adaptation score for channel 1 is 1.03, the model calculates the swing arm period sensitivity coefficient β1 = 0.15, and the cross-coupling coefficient β1 between the channel fluctuation factor and the sub-band interference coefficient is... 23 =-0.08, with the learning rate λ1 set to 0.2, the adjusted baseline weight value for the swing arm cycle becomes 0.6 + (0.2 × 0.15) = 0.63, which does not exceed the limit range; simultaneously, the compensation factor K corresponding to the interaction term in the weight correction matrix... 23 =1+(-0.08)=0.92.

[0038] Finally, by comparing the modified scenario adaptation scores of all available service channels, the channel with the highest score is selected as the target communication channel. Based on the center frequency, bandwidth and modulation method of the channel, the corresponding radio frequency tuning parameters are generated, and the radio frequency front end of the walkie-talkie is controlled to complete frequency synthesis and impedance matching, thereby establishing a stable communication link.

[0039] The corrected scene adaptation score is obtained based on the weight correction matrix, including: The weight correction matrix is ​​multiplied by the feature parameter vector of each service channel to obtain the weighted feature vector of each service channel. The elements of each dimension in the weighted feature vector are normalized and summed to generate a corrected scenario adaptation score for each service channel.

[0040] In this embodiment of the application, the weight correction matrix K is multiplied by the feature parameter vector of each service channel. In actual calculation, the feature parameter vector needs to be normalized to the interval [0,1] to obtain the weighted feature vector. Then, the elements of each dimension in the weighted feature vector are normalized and summed, and then multiplied by 100 to obtain the percentage score.

[0041] For example, if the weighted sum of channel 1 is 2.8, channel 2 is 2.2, and the maximum sum among all channels is 3.0, then the scene adaptation score after channel 1 correction is 2.8 / 3.0×100=93.3, and the scene adaptation score after channel 2 correction is 2.2 / 3.0×100=73.3.

[0042] Furthermore, after correcting the initial scene adaptation score weights, the following steps are taken: Obtain the network-wide channel status table, which includes at least the current occupancy identifier of each service channel, the statistical duty cycle during busy and idle periods, and the reserved priority flag; Based on the corrected scenario adaptation score, all service channels are sorted in descending order. According to the network-wide channel status table, channels currently occupied and marked as busy are removed, and channels with priority markers higher than the local communication level are reserved. The channel with the highest corrected scenario adaptation score is selected from the remaining idle service channels as the candidate channel. If the modified scenario adaptation score of the candidate channel is lower than the preset admission threshold, the reservation request mechanism of the network-wide channel status table is triggered to apply to the scheduling center for temporary release of the low-priority occupied channel, and after the reservation is successful, the released channel is confirmed as the target communication channel.

[0043] In this embodiment of the application, firstly, the local storage of the entire network channel status table is obtained. This table contains the current occupancy identifier of 16 service channels, the statistical duty cycle of busy and idle periods, and the reserved priority flag. The statistical duty cycle is specifically calculated by the percentage of time the channel has been occupied in the past hour. The reserved priority flag is divided into three levels: high, medium, and low, which are dynamically allocated by the scheduling center.

[0044] Secondly, all service channels are sorted in descending order of their corrected scenario adaptation scores. Then, channels marked as busy are removed according to the status table, and channels with a priority level higher than the user's communication level are also removed. The user's communication level is determined by the user's ID: low level for ordinary users, medium level for emergency users, and high level for command users. For example, if the user is an ordinary user, all high-priority reserved channels are removed, and the channel with the highest corrected scenario adaptation score is selected from the remaining idle channels as the candidate channel.

[0045] Secondly, the preset admission threshold can be set to 60, which is based on the lowest score that ensures an average opinion score of ≥3.5 in a large number of actual tests. If the modified scenario adaptation score of the candidate channel is greater than or equal to 60, it will be directly determined as the target communication channel. If the modified scenario adaptation score is less than 60, the reservation request mechanism will be triggered. Specifically, a request will be sent to the scheduling center to request the temporary release of a low-priority occupied channel, such as a channel marked as busy but with a low reserved priority and a duty cycle greater than 80%. This indicates that although the channel is busy, its utilization rate is extremely high and there may be intermittent idle periods. The scheduling center will return confirmation. If the reservation is successful, the released channel will be confirmed as the target communication channel.

[0046] The link maintenance module 14 is used to update the feature parameter set at preset intervals and calculate the scenario adaptation score of the current channel during communication maintenance, and to trigger power boost and modulation order reduction when the scenario adaptation score is lower than a preset threshold.

[0047] In this embodiment of the application, after communication is established, the current swing arm cycle, channel fluctuation factor and background noise distribution flatness are re-acquired at a preset time interval, such as every 0.5 seconds. The real-time scene adaptation score of the current channel is calculated according to the aforementioned method. The real-time scene adaptation score is compared with a preset threshold. If the real-time score is higher than or equal to the threshold, the current channel quality is determined to be good, and the existing radio frequency transmission power and modulation order are maintained unchanged. If the real-time score is lower than the threshold, the current channel is determined to be deteriorating, and the adaptive protection mechanism is immediately triggered.

[0048] Specifically, triggering power boost and modulation order reduction when the scene adaptation score is below a preset threshold includes: When the scenario adaptation score of the current channel is lower than the preset threshold, a timer is started to record the duration for which the score is lower than the preset threshold, and a power boosting operation is performed at the same time. If the scene adaptation score recovers to above the preset threshold within a preset time period, the timer is cleared and the current power level is maintained to continue communication; If the scenario adaptation score remains below the preset threshold for a preset duration and the current transmit power has reached the maximum transmit power limit, then the current power boost and modulation order downgrade operation will be terminated, and a channel renegotiation request will be initiated.

[0049] In this embodiment of the application, firstly, during the communication maintenance period, the above steps of collecting characteristic parameters are repeated at preset intervals, and the scenario adaptation score of the current channel is calculated. At the same time, a maintenance threshold is preset. The preset threshold can be set to 50. If it is lower than the preset threshold, it means that a certain decrease can be tolerated but it should not be too low. The threshold is set based on the average score corresponding to the critical point of sudden increase in bit error rate in historical measured data.

[0050] If the current channel's scenario adaptation score is below 50, start a timer to record the duration of the score being below the threshold, and immediately perform power boost and modulation order downgrade. For example, increase the transmit power by 2dB from the current value, with a preset step size of 2dB and a maximum of 25dBm; reduce the modulation order from 4FSK to 2FSK to reduce the signal-to-noise ratio requirement, but at the expense of data rate.

[0051] If the scene adaptation score recovers to above the preset threshold within a preset duration of 2 seconds, the timer is cleared, the current power level is maintained, and communication continues without dropping back to the original value to prevent frequent fluctuations. If the scene adaptation score remains below 50 within 2 seconds, and the current transmit power has reached the maximum value of 25dBm, since there is no room for improvement, the power boost and modulation downgrade operations are terminated, and a channel renegotiation request is initiated instead.

[0052] For example, if the scene adaptation score is 45 at a certain moment, the power is increased by 2dB to 22dBm and the order is reduced to 2FSK. After 1.5 seconds, the scene adaptation score rises to 52, then the 22dBm and 2FSK are maintained. If the score is still 46 after 2 seconds and the power has reached 25dBm, then renegotiation is initiated.

[0053] The channel renegotiation request includes: Select currently idle and unreserved service channels from the network channel status table, and sort them from high to low according to the modified scenario adaptation score of each channel; Select the idle channel with the highest scene adaptation score after correction as the target channel for switching, send a switching request to the scheduling center, and after receiving confirmation, switch the communication link from the current channel to the target channel for switching, while restoring the transmission power to the initial default value and restarting the monitoring process during communication maintenance.

[0054] In this embodiment of the application, all currently idle and unreserved service channels are selected from the network channel status table, and sorted from high to low according to the latest calculated scene adaptation score of each channel. The scene adaptation score is continuously updated in the background.

[0055] Secondly, the idle channel with the highest scenario adaptation score is selected as the target channel for switching, assuming it is channel 8. The source walkie-talkie sends a switching request to the dispatch center, carrying the current channel identifier and the target channel identifier. After the dispatch center returns confirmation, the system will hop the communication link from the current channel to channel 8, restore the transmit power to the initial default value of 20dBm, restore the modulation mode to 4FSK, clear the timer, and restart the monitoring process during the communication maintenance period.

[0056] Furthermore, a short-range walkie-talkie channel adaptive tuning system also includes a non-communication walkie-talkie in a standby state: The non-communication walkie-talkie periodically wakes up the receiver to perform energy detection and synchronization sequence matching on the preamble signal of the target communication channel; If a preamble signal is detected and the synchronization sequence is successfully matched, the channel frequency and time slot allocation information in the radio frequency tuning parameters are parsed, and the channel priority in the local watch channel list is updated synchronously. If no valid preamble signal is detected for several consecutive cycles, the wake-up frequency is reduced and the system enters deep wait mode until a wake-up instruction is received.

[0057] In this embodiment, the non-communication walkie-talkie wakes up the receiver at a preset period of seconds while in standby mode. For example, the preset period can be set to 1 second and the duration is 100ms. During this period, energy detection and synchronization sequence matching are performed on the preamble signal of the target communication channel. Specifically, the received signal strength indication is calculated and compared with a preset energy detection threshold. If it is higher than the preset energy threshold, the synchronization sequence in the preamble is further extracted and the correlation is calculated with the locally stored standard synchronization sequence. If the matching degree exceeds the preset synchronization sequence matching threshold, it is determined to be a valid preamble signal.

[0058] The energy detection threshold is the minimum threshold used to determine whether there is valid preamble energy in the received signal. Received signals below this threshold are considered pure noise or spurious interference, and the receiver will not initiate the subsequent synchronization sequence matching process to avoid false wake-ups and invalid calculations. The setting of this threshold needs to balance the detection probability and the false alarm probability. It is usually based on the receiver's noise floor level and leaves a certain margin to combat fast fading and shadowing effects. It takes into account the typical receiving sensitivity of the walkie-talkie and the thermal noise power in short-range environments. For example, the energy detection threshold can be set to -105dBm to -100dBm in normal environments, -95dBm to -90dBm in strong interference or complex indoor environments, and can be relaxed to -110dBm in extreme sensitivity scenarios.

[0059] The synchronization sequence matching threshold is the minimum threshold used to determine the similarity between the detected synchronization sequence in the preamble and the locally stored standard synchronization sequence. This threshold is used to exclude pseudo-correlation peaks generated by non-local signals or random noise. It is set based on the autocorrelation and cross-correlation characteristics of pseudo-random synchronization sequences, with the main autocorrelation peak being significantly higher than the secondary peaks. For example, the high-reliability synchronization sequence matching threshold is 0.75~0.85, which is suitable for low-power standby modes that are sensitive to false wake-ups. The normal synchronization sequence matching threshold is 0.65~0.75, which is suitable for ordinary sensitivity scenarios. The synchronization sequence matching threshold for low signal-to-noise ratio tolerance modes can be set to 0.50~0.65, which requires multiple confirmation mechanisms to prevent false triggering.

[0060] Secondly, if a preamble signal is detected and the synchronization sequence is successfully matched, the channel frequency and time slot allocation information in the RF tuning parameters are parsed, and the channel priority in the local waiting channel list is updated synchronously, increasing the priority of the currently used channel.

[0061] If no valid preamble signal is detected for several consecutive cycles, such as five consecutive cycles, the wake-up cycle will be reduced from 1 second to 5 seconds, the receiver turn-on time will be shortened to 50ms, and the system will enter deep standby mode until a wake-up instruction, such as a broadcast call indicator, is received from the dispatch center.

[0062] Furthermore, in a short-range walkie-talkie channel adaptive tuning system, link release includes: When the source walkie-talkie detects a communication termination command or when the scenario adaptation score is lower than the emergency interruption threshold multiple times during the communication maintenance period, it sends a link release request to the dispatch center. The link release request includes at least the channel identifier of the current communication channel and the actual communication quality summary information for this communication period. After receiving the release confirmation from the dispatch center, the radio frequency resources on the current communication channel are released and the transmit power is restored to the standby default value; After the release is completed, the information of the target communication channel used in this communication, the communication start and end timestamps, and the release reason tag are sent to the maintenance node of the network-wide channel status table, triggering the real-time update of the network-wide channel status table, switching the current occupancy flag of the corresponding service channel from busy to idle, and updating the duty cycle and historical score records according to the actual communication quality summary information of this communication period. After the maintenance node completes the update, it broadcasts a channel status change notification to all non-communication walkie-talkies in the standby state.

[0063] This application describes resource reclamation and status updates when communication ends normally or is abnormally interrupted. First, when the source walkie-talkie detects a communication termination command, such as when the user presses the hang-up button, the call times out and automatically disconnects, or when the scenario adaptation score is lower than the emergency interruption threshold multiple times during the communication maintenance period, it is determined that the communication is abnormally interrupted. The emergency interruption threshold can be set to 30, which is based on the critical point where the bit error rate causes the voice to become completely incomprehensible. At this time, the source walkie-talkie immediately sends a link release request to the dispatch center. The request data packet contains at least the channel identifier of the current communication channel, the start and end timestamps of this communication, the average scenario adaptation score, the maximum transmit power usage record, and the interruption reason label.

[0064] Secondly, upon receiving the release confirmation command from the dispatch center, the source walkie-talkie immediately disconnects the radio frequency transmission path, releases the radio frequency resources occupied on the current communication channel, and quickly restores the transmission power to the standby default value to reduce power consumption and minimize potential interference to other channels. Subsequently, the source walkie-talkie packages and sends the key metadata of this communication, including the target communication channel ID, communication start and end timestamps, release reason tag, and communication quality summary information, to the maintenance node of the network-wide channel status table.

[0065] Secondly, after receiving the above data, the maintenance node triggers the real-time update process of the entire network channel status table. Specifically, it first switches the current occupancy flag of the corresponding service channel from busy to idle, so that it can be reselected by other terminals. Then, it updates the statistical duty cycle of the channel according to the actual duration of this communication period, i.e., new duty cycle = (original cumulative occupancy time + duration of this communication) / total duration of the statistical period. At the same time, the average scenario adaptation score of this communication is included in the historical score record, and the long-term quality assessment value of the channel is updated using a moving average algorithm for reference in the dynamic adjustment of the subsequent weight correction matrix.

[0066] Furthermore, after the network-wide channel status table is updated, the maintenance node broadcasts a channel status change notification to all non-communication walkie-talkies in a standby state via the control channel. This notification includes the changed channel identifier and its new status information, such as idle / busy status and priority flag. Upon receiving the broadcast notification, the non-communication walkie-talkies parse and update their locally stored copy of the network-wide channel status table, ensuring that all terminals in the network have a consistent understanding of channel resources. This avoids conflicts caused by multiple terminals simultaneously competing for the same channel, achieving efficient recycling of channel resources.

[0067] In summary, compared to existing technologies, this application achieves a quantitative assessment of the complexity of short-range communication environments by constructing a scenario adaptation scoring model based on multi-dimensional feature parameter vectors and dynamic weight correction matrices. This overcomes the limitations of traditional channel selection relying solely on signal strength. By introducing a real-time interaction mechanism of the entire network channel state table and combining a collaborative filtering strategy based on user communication level and channel reservation priority, it effectively solves the problems of channel conflict and uneven resource allocation in high-load scenarios. Furthermore, the link maintenance module proposed in this application adopts a dual protection mechanism of adaptive power boosting and modulation order reduction, and triggers channel renegotiation based on scoring ranking under extreme degradation conditions, thereby improving the robustness and continuity of the communication link.

[0068] In summary, the embodiments of this application have at least the following technical effects: This application provides a short-range walkie-talkie channel adaptive tuning system. First, it comprehensively characterizes the dynamic changes in the short-range communication environment by introducing multi-dimensional feature parameters such as the human walking arm swing period, the sliding window variance of the received signal strength indication value, and the flatness of the background noise power spectrum distribution within the speech band. Second, it constructs a multiple linear regression model using historical communication data, extracting sensitivity coefficients and cross-coupling coefficients to dynamically correct the initial scoring weights, thus realizing the transformation from static rules to a dynamic adaptive strategy. Finally, by monitoring the scene adaptation score in real time during communication maintenance and combining it with graded response mechanisms such as power boosting, modulation order reduction, and channel renegotiation, a closed-loop link maintenance strategy is formed. This not only ensures communication continuity when channel quality is slightly degraded but also allows for rapid switching to a better channel when severely degraded, significantly reducing the drop rate and bit error rate, improving the utilization efficiency of spectrum resources, and enhancing the overall robustness of the communication system.

[0069] Through the above technical solution, this application quantifies the impact of human arm swinging motion on signal reception and performs a weighted evaluation based on the spectral characteristics of background noise. This effectively avoids misjudgments caused by fluctuations in a single signal strength index, improving the accuracy and stability of channel selection. Simultaneously, the hierarchical link maintenance and renegotiation strategy minimizes unnecessary channel switching and power consumption while ensuring communication quality, extending the battery life of terminal devices. This makes it suitable for practical applications with extremely high communication reliability requirements and complex environments, such as emergency command and security for large-scale events.

Claims

1. A short-range walkie-talkie channel adaptive tuning system, characterized in that, The system includes: The feature parameter acquisition module is used to respond to the communication request initiated by the source walkie-talkie and acquire the feature parameter set in the current short-range environment. The feature parameter set includes the human walking arm swing period, the sliding window variance of the received signal strength indication value, and the distribution flatness of the background noise power spectrum in the speech band. The initial score calculation module is used to calculate the initial scenario adaptation score of each service channel based on the feature parameter set, wherein the weight coefficient of the distribution flatness is positively correlated with the human walking arm swing period, and the weight coefficient of the sliding window variance is negatively correlated with the human walking arm swing period. The communication channel selection module is used to extract sensitivity coefficients and cross-coupling coefficients based on the mapping relationship between various characteristic parameters and actual communication quality indicators during historical communication periods, and to correct the initial scene adaptation score weights based on the sensitivity coefficients and cross-coupling coefficients. The channel with the highest scene adaptation score after correction is determined as the target communication channel, and radio frequency tuning parameters are generated to establish a communication link. The link maintenance module is used to update the feature parameter set at preset intervals and calculate the scenario adaptation score of the current channel during communication maintenance. When the scenario adaptation score is lower than a preset threshold, it triggers power boost and modulation order downgrade.

2. The short-range walkie-talkie channel adaptive tuning system according to claim 1, characterized in that, The initial scenario adaptation score for each service channel is calculated based on the aforementioned feature parameter set, including: The human walking arm swing cycle is divided into a low-speed swing interval, a medium-speed swing interval, and a high-speed swing interval, and a weighted benchmark value of the arm swing cycle is preset for each interval. The variance of the sliding window is normalized to a channel fluctuation factor, and the reliability coefficient of the received signal strength indication value is dynamically adjusted according to the channel fluctuation factor. The flatness of the background noise power spectrum distribution within the speech band is converted into subband interference coefficients; Based on the swing arm period weight benchmark value, the credibility coefficient and the sub-band interference coefficient are weighted and summed to obtain the initial scenario adaptation score for each service channel.

3. The short-range walkie-talkie channel adaptive tuning system according to claim 1, characterized in that, Based on the mapping relationship between various characteristic parameters and actual communication quality indicators within historical communication periods, sensitivity coefficients and cross-coupling coefficients are extracted, including: Obtain the feature parameter sequences and corresponding actual communication quality indicators for multiple recent historical communication periods. The actual communication quality indicators include at least bit error rate, signal-to-noise ratio, and voice intelligibility. A multiple linear regression model is constructed using the feature parameter sequence as input and the actual communication quality index as output. The independent influence factors of each feature parameter on communication quality are extracted from the multiple linear regression model as the sensitivity coefficient, and the interaction influence factors between different feature parameters are extracted as the cross-coupling coefficient.

4. The short-range walkie-talkie channel adaptive tuning system according to claim 3, characterized in that, Based on the sensitivity coefficient and the cross-coupling coefficient, the initial scene adaptation score weights are corrected, including: The sensitivity coefficient is used as a first-order correction factor for each feature parameter to adjust the weight of the corresponding feature parameter in the initial scene adaptation score. The cross-coupling coefficient is used as a second-order compensation factor and is multiplied and superimposed on the associated feature parameter weights to form a weight correction matrix. The corrected scene adaptation score is obtained based on the weight correction matrix.

5. A short-range walkie-talkie channel adaptive tuning system according to claim 4, characterized in that, The corrected scene adaptation score is obtained based on the weight correction matrix, including: The weight correction matrix is ​​multiplied by the feature parameter vector of each service channel to obtain the weighted feature vector of each service channel. The elements of each dimension in the weighted feature vector are normalized and summed to generate a corrected scenario adaptation score for each service channel.

6. A short-range walkie-talkie channel adaptive tuning system according to claim 4, characterized in that, After correcting the initial scene adaptation score weights, the following is included: Obtain the network-wide channel status table, which includes at least the current occupancy identifier of each service channel, the statistical duty cycle during busy and idle periods, and the reserved priority flag; Based on the corrected scenario adaptation score, all service channels are sorted in descending order. According to the network-wide channel status table, channels currently occupied and marked as busy are removed, and channels with priority markers higher than the local communication level are reserved. The channel with the highest corrected scenario adaptation score is selected from the remaining idle service channels as the candidate channel. If the modified scenario adaptation score of the candidate channel is lower than the preset admission threshold, the reservation request mechanism of the network-wide channel status table is triggered to apply to the scheduling center for temporary release of the low-priority occupied channel, and after the reservation is successful, the released channel is confirmed as the target communication channel.

7. A short-range walkie-talkie channel adaptive tuning system according to claim 1, characterized in that, When the scene adaptation score is lower than a preset threshold, power boosting and modulation order reduction are triggered, including: When the scenario adaptation score of the current channel is lower than the preset threshold, a timer is started to record the duration for which the score is lower than the preset threshold, and a power boosting operation is performed at the same time. If the scene adaptation score recovers to above the preset threshold within a preset time period, the timer is cleared and the current power level is maintained to continue communication; If the scenario adaptation score remains below the preset threshold for a preset duration and the current transmit power has reached the maximum transmit power limit, then the current power boost and modulation order downgrade operation will be terminated, and a channel renegotiation request will be initiated.

8. A short-range walkie-talkie channel adaptive tuning system according to claim 7, characterized in that, The channel renegotiation request includes: Select currently idle and unreserved service channels from the network channel status table, and sort them from high to low according to the modified scenario adaptation score of each channel; Select the idle channel with the highest scene adaptation score after correction as the target channel for switching, send a switching request to the scheduling center, and after receiving confirmation, switch the communication link from the current channel to the target channel for switching, while restoring the transmission power to the initial default value and restarting the monitoring process during communication maintenance.

9. A short-range walkie-talkie channel adaptive tuning system according to claim 6, characterized in that, Non-communication walkie-talkies in standby mode include: The non-communication walkie-talkie periodically wakes up the receiver to perform energy detection and synchronization sequence matching on the preamble signal of the target communication channel; If a preamble signal is detected and the synchronization sequence is successfully matched, the channel frequency and time slot allocation information in the radio frequency tuning parameters are parsed, and the channel priority in the local watch channel list is updated synchronously. If no valid preamble signal is detected for several consecutive cycles, the wake-up frequency is reduced and the system enters deep wait mode until a wake-up instruction is received.

10. A short-range walkie-talkie channel adaptive tuning system according to claim 1, characterized in that, Link release includes: When the source walkie-talkie detects a communication termination command or when the scenario adaptation score is lower than the emergency interruption threshold multiple times during the communication maintenance period, it sends a link release request to the dispatch center. The link release request includes at least the channel identifier of the current communication channel and the actual communication quality summary information for this communication period. After receiving the release confirmation from the dispatch center, the radio frequency resources on the current communication channel are released and the transmit power is restored to the standby default value; After the release is completed, the information of the target communication channel used in this communication, the communication start and end timestamps, and the release reason tag are sent to the maintenance node of the network-wide channel status table, triggering the real-time update of the network-wide channel status table, switching the current occupancy flag of the corresponding service channel from busy to idle, and updating the duty cycle and historical score records according to the actual communication quality summary information of this communication period. After the maintenance node completes the update, it broadcasts a channel status change notification to all non-communication walkie-talkies in the standby state.

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