A data transmission anomaly detection method and system for a smart converged terminal

Abstract

The application relates to the technical field of digital information transmission, and discloses a data transmission anomaly detection method and system for an intelligent fusion terminal, wherein the method comprises the following steps: obtaining a detection response signal in a nonlinear power ramp emission mode according to a terminal sleep-wake state; extracting automatic gain control dynamic characteristic information; performing coherent channel estimation and subband discrete analysis on the information and the detection response signal to obtain a frequency domain dispersion index FCDI; determining a link state by combining an automatic gain control anomaly index AGCI and the frequency domain dispersion index FCDI; and generating a transmission control instruction according to the determination result and the dynamic characteristic information. Compared with a detection mode of multiple handshakes or a single receiving strength, the application jointly represents receiver gain transients and channel frequency domain local anomalies in one wake-up detection, adjusts re-detection waiting, a decision threshold and model parameters according to the representation, shortens the evaluation time, and reduces the influence of wake-up transient misjudgment on transmission.

Description

Technical Field

[0001] This invention relates to the field of digital information transmission technology, and in particular to a method and system for detecting abnormal data transmission in intelligent converged terminals. Background Technology

[0002] Currently, in scenarios such as the Internet of Things for power systems and industrial wireless sensing, a large number of battery-powered smart converged terminals extend their battery life by periodically going into sleep mode. After being woken up, the terminal needs to rebuild the wireless link and complete the transmission of collected data within a very short time. To ensure communication reliability, existing technologies typically use methods such as multiple statistical averaging based on received signal strength indicators or packet error rate counting based on handshake frames to detect link availability. However, these methods generally require sending multiple probe frames and going through multiple transmit and receive cycles, resulting in long decision delays, which are difficult to adapt to the stringent requirements of fast transmission in low-power sleep-wake scenarios.

[0003] For example, when a converged terminal wakes up from deep sleep, the processes of crystal oscillator startup, phase-locked loop locking, and power amplifier bias establishment cause drastic changes in the characteristics of the transmit and receive channels within milliseconds. Simultaneously, the receiver's automatic gain control loop experiences overshoot or oscillation due to the sudden signal appearance, leading to severe fluctuations in channel gain and a significant increase in frequency-selective fading. Conventional link detection mechanisms require statistical convergence times of hundreds of microseconds or even milliseconds and rely on multiple handshake interactions for confirmation, completely ignoring the drastic changes in channel characteristics at the moment of wake-up. Forcibly sending data frames before automatic gain control has stabilized or before deep channel fading has subsided can easily lead to high bit error rates or even connection failures, wasting energy and forcing the terminal to repeatedly retransmit, severely reducing transmission efficiency and battery life.

[0004] Therefore, there is an urgent need for a method that can quickly determine the transient availability anomaly of the link using a very small number of probe signals within a very short preamble time after the converged terminal wakes up from sleep. This would enable the terminal to accurately identify transient unavailable links even under drastic channel changes caused by wake-up shocks, actively transmit with micro-delay and adaptively adjust the detection sensitivity, avoid invalid transmission when the channel is unstable, and thus improve the success rate and power efficiency of immediate transmission after wake-up. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to propose a data transmission anomaly detection method for intelligent converged terminals. This method addresses the problem that conventional link detection requires multiple handshakes and statistical analysis, resulting in excessive time consumption and difficulty in adapting to sudden channel changes upon wake-up. In particular, it fails to quickly detect instantaneous link availability anomalies using only a very small number of probe signals when low-power converged terminals wake up from periodic sleep and immediately transmit data.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for detecting abnormal data transmission in intelligent converged terminals.

[0007] The method for detecting data transmission anomalies in intelligent fusion terminals includes: Step S10: Obtain the terminal sleep / wake-up state of the intelligent fusion terminal, and based on the terminal sleep / wake-up state, execute the detection-triggered transmission task using a preset nonlinear power ramp transmission mechanism, and output the detection response signal; Step S20: Based on the detection response signal, an automatic gain control transient recording mechanism is used to perform a gain adjustment feature extraction task and output automatic gain control dynamic feature information; Step S30: Based on the automatic gain control dynamic characteristic information and the detection response signal, a coherent channel estimation and subband discrete analysis mechanism is used to perform frequency domain preprocessing tasks and output the frequency domain discreteness index FCDI. Step S40: Based on the automatic gain control dynamic feature information and the frequency domain dispersion index FCDI, a weighted fusion decision mechanism is used to perform the link availability determination task and output the link status determination result; Step S50: Based on the link status determination result and the automatic gain control dynamic characteristic information, an adaptive anomaly handling mechanism is used to execute the transmission control task and output the transmission control command.

[0008] Preferably, step S10, which involves obtaining the terminal sleep / wake-up state of the intelligent fusion terminal, executing a detection-triggered transmission task based on the terminal sleep / wake-up state using a preset nonlinear power ramp transmission mechanism, and outputting a detection response signal, specifically includes: Step S101: When the terminal sleep-wake state characterizes the intelligent fusion terminal switching from sleep state to wake state, a detection excitation is generated that sequentially includes a preamble power ramp transmission segment, a multi-subcarrier training sequence, and a silent protection interval. Step S102: Divide the preamble power ramp transmission segment into at least three sequentially arranged power stages with the same symbol duration. Set the transmission power of each power stage in a non-uniform increment manner, so that the transmission power difference between adjacent power stages below the preset power threshold is less than the transmission power difference between adjacent power stages. Determine the difference between the receiver sensitivity lower limit and the preset power margin as the initial transmission power, and set the termination transmission power according to the condition that the receiver input power corresponding to the termination power stage does not exceed the maximum allowable input power of the receiver, thus obtaining the preamble power ramp transmission segment. Step S103: After the preamble power ramp transmission segment, the multi-subcarrier training sequence is transmitted, and multiple non-zero training symbols are modulated onto the corresponding subcarriers. After the multi-subcarrier training sequence, the silence protection interval is set, and the receiving end collects the received data corresponding to the detection excitation to form the detection response signal.

[0009] Preferably, step S20, which involves performing gain adjustment feature extraction based on the detection response signal using an automatic gain control transient recording mechanism and outputting automatic gain control dynamic feature information, specifically includes: Step S201: Before the detection response signal arrives at the receiving end, the receiving end is configured to a low-power listening state, and when the starting position of the detection response signal is detected, a gain transient recording window corresponding to the detection response signal is established; Step S202: According to the transmit power change corresponding to the detection response signal, the gain transient recording window is divided into multiple power stages, and the number of gain adjustments and the stage termination gain of the automatic gain control unit at the receiving end in each power stage are recorded respectively; the power stage where the number of gain adjustments exceeds the preset maximum jitter number is marked as a jitter stage, the number of jitter stages is counted, and the number of gain adjustments, the stage termination gain and the number of jitter stages are arranged according to the order of each power stage to obtain the automatic gain control transient feature vector; Step S203: Input the automatic gain control transient feature vector into the pre-trained principal component analysis mapping function to obtain the automatic gain control anomaly mapping result. Normalize the automatic gain control anomaly mapping result to the value range of 0 to 1 to obtain the automatic gain control anomaly index AGCI. Combine the automatic gain control transient feature vector and the automatic gain control anomaly index AGCI to form the automatic gain control dynamic feature information.

[0010] Preferably, in step S202, the process of setting the preset maximum number of jitters includes: Obtain the gain switching time required for the automatic gain control unit to complete one gain switching, and obtain the stage duration corresponding to a single power stage; Calculate the ratio of the gain switching time to the stage duration, and round the ratio up to obtain the candidate jitter count; When the number of candidate jitters is less than 1, the preset maximum number of jitters is set to 1; when the number of candidate jitters is greater than or equal to 1, the preset maximum number of jitters is set to the number of candidate jitters.

[0011] Preferably, step S30, which involves performing frequency domain preprocessing based on the automatic gain control dynamic characteristic information and the detection response signal using a coherent channel estimation and subband discrete analysis mechanism to output the frequency domain dispersion index FCDI, specifically includes: Step S301: Perform symbol synchronization and subcarrier demodulation on the detection response signal, extract multiple subcarrier received symbols, and obtain the preset non-zero training symbols corresponding to each subcarrier received symbol; divide each subcarrier received symbol by the corresponding preset non-zero training symbol to obtain the channel frequency response estimate value corresponding to each subcarrier. Step S302: Extract the stage termination gain corresponding to each power stage from the automatic gain control dynamic feature information, convert the stage termination gain into a linear amplitude gain, and determine the linear amplitude gain corresponding to each subcarrier according to the power stage to which each subcarrier received symbol belongs; divide the amplitude of each channel frequency response estimate by the corresponding linear amplitude gain to obtain the gain normalization amplitude of each subcarrier; obtain the root mean square delay spread of the deployment environment where the intelligent fusion terminal is located, determine the coherent bandwidth according to the root mean square delay spread, divide each subcarrier into multiple non-overlapping continuous sub-bands according to the condition that the frequency span of each sub-band is no greater than half of the coherent bandwidth, and calculate the mean and variance of the gain normalization amplitude in each continuous sub-band respectively; Step S303: For each continuous sub-band, divide the variance of the gain normalization amplitude within the continuous sub-band by the sum of the square of the mean of the gain normalization amplitude within the continuous sub-band and the preset regularization constant to obtain the corresponding sub-band dispersion; select the maximum value from the sub-band dispersion corresponding to each continuous sub-band as the frequency domain dispersion index FCDI output, wherein the preset regularization constant is a positive number.

[0012] Preferably, step S40, which involves performing a link availability determination task based on the automatic gain control dynamic characteristic information and the frequency domain dispersion index FCDI, and outputting the link status determination result using a weighted fusion decision mechanism, specifically includes: Step S401: Extract the Automatic Gain Control Anomaly Index (AGCI) from the automatic gain control dynamic feature information, and normalize the frequency domain dispersion index (FCDI) according to a preset frequency domain dispersion reference value to obtain the normalized frequency domain dispersion index:

[0013] in, The normalized frequency domain dispersion index is... The preset frequency domain dispersion reference value is a positive number. Step S402: Perform weighted fusion of the normalized frequency domain dispersion index and the automatic gain control anomaly index AGCI to obtain the link anomaly judgment value:

[0014] in, This is the link anomaly judgment value. For frequency domain dispersion weights, For automatic gain control abnormal weights, , ,and The frequency domain dispersion weight and the automatic gain control anomaly weight are determined based on the receiver operating characteristic curve analysis results of the training samples. Step S403: Compare the link anomaly judgment value with the preset anomaly judgment threshold, and determine the link status identifier according to the following formula:

[0015] in, This is the link status identifier. A preset anomaly judgment threshold is set; when the link status identifier is zero, it indicates that the link is available; when the link status identifier is one, it indicates that the link is abnormal; the link status identifier and the link anomaly judgment value are combined to form the link status judgment result.

[0016] Preferably, step S50, which involves executing the transmission control task and outputting the transmission control command using an adaptive anomaly handling mechanism based on the link state determination result and the automatic gain control dynamic characteristic information, specifically includes: Step S501: Extract the link status identifier from the link status determination result. When the link status identifier indicates a link anomaly, input the automatic gain control dynamic feature information into a pre-trained regression model to obtain the stable time constant of the communication impulse response. When the stable time constant is less than a preset minimum waiting time, set the delay reprobe waiting time to the preset minimum waiting time. When the stable time constant is greater than a preset maximum waiting time, set the delay reprobe waiting time to the preset maximum waiting time. When the stable time constant is between the preset minimum waiting time and the preset maximum waiting time, set the delay reprobe waiting time to the stable time constant and generate a delay reprobe sub-instruction containing the delay reprobe waiting time. Step S502: Obtain the block error rate corresponding to each data transmission within a preset statistical window, and calculate the window average block error rate corresponding to the preset statistical window; when the window average block error rate is greater than a preset target block error rate, And when the link anomaly judgment value corresponding to each data transmission within the preset statistical window is less than the preset anomaly judgment threshold, Multiply the preset anomaly judgment threshold by the preset threshold reduction factor to obtain the updated anomaly judgment threshold, and generate a threshold adjustment sub-instruction containing the updated anomaly judgment threshold, wherein the preset threshold reduction factor is greater than zero and less than 1. Step S503: At the end of each preset deployment and maintenance cycle, obtain samples of confirmed link status within the preset deployment and maintenance cycle, and use the samples to update the regression model, the frequency domain dispersion weight, the automatic gain control anomaly weight, and the preset anomaly decision threshold to obtain the decision parameter update result; generate the transmission control instruction based on the delay reprobe sub-instruction, the threshold adjustment sub-instruction, and the decision parameter update result; if the delay reprobe sub-instruction, the threshold adjustment sub-instruction, and the decision parameter update result are not generated, generate a transmission control instruction to maintain the current data transmission status.

[0017] The present invention also provides a data transmission anomaly detection system for intelligent converged terminals, the data transmission anomaly detection system for intelligent converged terminals comprising: The detection trigger module is used to obtain the terminal sleep / wake-up state of the intelligent fusion terminal, and to perform the detection trigger transmission task based on the terminal sleep / wake-up state using a preset nonlinear power ramp transmission mechanism, and output the detection response signal. The transient feature extraction module is used to perform gain adjustment feature extraction tasks based on the detection response signal using an automatic gain control transient recording mechanism, and output automatic gain control dynamic feature information; The frequency domain preprocessing module is used to perform frequency domain preprocessing tasks based on the automatic gain control dynamic characteristic information and the detection response signal, using a coherent channel estimation and subband discrete analysis mechanism, and outputs the frequency domain discreteness index FCDI. The fusion decision module is used to perform a link availability determination task based on the automatic gain control dynamic feature information and the frequency domain dispersion index FCDI, and output the link status determination result by adopting a weighted fusion decision mechanism. The anomaly handling module is used to execute transmission control tasks and output transmission control commands by adopting an adaptive anomaly handling mechanism based on the link status determination result and the automatic gain control dynamic characteristic information.

[0018] The present invention also provides a data transmission anomaly detection device for a smart converged terminal. The data transmission anomaly detection device for a smart converged terminal includes: a memory, a processor, and a data transmission anomaly detection program for a smart converged terminal stored in the memory and executable on the processor. When the data transmission anomaly detection program for a smart converged terminal is executed by the processor, it implements the above-described method.

[0019] The present invention also provides a computer program product, the computer program product including a data transmission anomaly detection program for a smart converged terminal, the data transmission anomaly detection program for a smart converged terminal implementing the above method when executed by a processor.

[0020] The beneficial effects of this invention are as follows: 1. This invention generates a detection excitation sequence that sequentially includes a preamble power ramp transmit segment, a multi-subcarrier training sequence, and a silent protection interval when the intelligent fusion terminal switches from a dormant state to a wake-up state. The preamble power ramp employs a nonlinear incremental approach with small steps in the low-power segment and large steps in the high-power segment, and the initial transmit power and termination transmit power are constrained by the receiver sensitivity lower limit and the maximum allowable input power, respectively. This allows weak signal acquisition, gain establishment, and normal power input processes to be covered in a single continuous detection, enabling the receiver to simultaneously obtain the power stage boundaries required for gain transient recording and the multi-subcarrier training data required for coherent channel estimation, reducing the repeated handshakes and timing differences caused by using multiple fixed-power detection frames.

[0021] 2. This invention records the number of gain adjustments, stage termination gains, and jitter stages of the automatic gain control unit according to the power stages of the detection response signal, and obtains the automatic gain control anomaly index AGCI through principal component analysis. Then, the stage termination gain is used to normalize the channel frequency response amplitude of each subcarrier, and continuous subbands are divided according to coherence bandwidth constraints. The maximum value is selected from the dispersion of each subband to form the frequency domain dispersion index FCDI. Finally, the AGCI and the normalized FCDI are weighted and fused. This process quantifies the wake-up transient anomalies of the receiver gain loop and the local frequency domain anomalies of the propagation channel separately before joint determination. This avoids automatic gain changes being mistakenly recorded as channel amplitude fluctuations and prevents local deep fading from being masked by the full-band average value, thus providing both dynamic data from the receiver front-end and frequency domain structure data for link state determination.

[0022] 3. When the link status identifier indicates an anomaly, this invention predicts the stable time constant of the communication impulse response based on the dynamic characteristic information of automatic gain control, and determines the delay reprobe waiting time between the preset minimum waiting time and the preset maximum waiting time. Simultaneously, it identifies consecutive missed detections based on the average block error rate within the statistical window and the corresponding link anomaly judgment value. When conditions are met, the anomaly judgment threshold is reduced, and after each deployment and maintenance cycle, the regression model, fusion weights, and judgment thresholds are updated using samples of confirmed link statuses. This incorporates instantaneous anomaly recovery, long-term block error feedback, and statistical changes in the deployment environment into waiting control, threshold adjustment, and periodic parameter updates, respectively. This reduces transmission delays caused by consecutive failures due to excessively short fixed waiting times or excessively long waiting times, and lowers the risk of missed detections resulting from long-term use of mismatched judgment parameters. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a first embodiment of a data transmission anomaly detection method for a smart fusion terminal according to the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0025] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: As Figure 1 The diagram shown is a flowchart of a first embodiment of a data transmission anomaly detection method for a smart converged terminal according to the present invention.

[0027] In the first embodiment, the data transmission anomaly detection method for a smart fusion terminal includes: Step S10: Obtain the terminal sleep / wake-up state of the intelligent fusion terminal, and based on the terminal sleep / wake-up state, execute the detection-triggered transmission task using a preset nonlinear power ramp transmission mechanism, and output the detection response signal; The terminal sleep / wake-up state describes whether the intelligent fusion terminal has switched from a sleep state (reducing RF and baseband workload) to a wake-up state capable of initiating link probing. This state can be provided by the terminal's existing sleep control process during state switching and serves as the trigger condition for this round of probing excitation. Only when the state representation has completed the wake-up switch is a probing excitation consisting of a preamble power ramp transmit segment, a multi-subcarrier training sequence, and a silence guard interval generated sequentially, enabling the receiver to obtain continuous observation data during the same wake-up process.

[0028] The preamble power ramp transmit phase consists of multiple symbol durations with sequentially increasing transmit power. At least three power phases are set; adjacent phases below a preset power threshold use a smaller power difference, while phases above the threshold use a larger power difference. The smaller power difference is used to refine input variations near the receiver sensitivity, while the larger power difference is used to quickly cover the normal operating power after the receiver has acquired the signal. The preset power threshold can be determined based on the input power corresponding to when the receiver enters the stable gain adjustment range, but the actual power of each phase is still constrained by both the initial and final power.

[0029] The initial transmit power is determined by subtracting a preset power margin from the receiver's lower sensitivity limit, ensuring that the first power phase can cover the weak signal input that may occur during the initial wake-up phase. The termination transmit power is set so that the receiver's input power at the corresponding termination phase does not exceed the receiver's maximum allowable input power, preventing saturation of the receiver front-end due to excessive input in the initial high-gain state. The receiver's lower sensitivity limit and maximum allowable input power can be obtained from the receiver calibration parameters, and the power margin is pre-configured based on the allowable loss fluctuations of the deployed link. Therefore, the start point, end point, and inter-phase changes of the nonlinear ramp all have executable and definite criteria.

[0030] The multi-subcarrier training sequence is transmitted immediately after the power ramp. Multiple non-zero training symbols are modulated onto the corresponding subcarriers, enabling the receiver to estimate the channel response of each subcarrier given the transmitted symbols. The silence guard interval following the training sequence does not carry the training symbols to be detected and is used to isolate the probe excitation from the subsequent transmission process. The receiver collects data according to the timing of the probe excitation to form a probe response signal; this signal retains both the gain changes caused by each power stage and the multi-subcarrier received symbols required for frequency domain estimation, thus it can be used by both S20 and S30 simultaneously.

[0031] Fixed-power detection can only observe the receiver's results under a single input strength, and cannot distinguish between gain overshoot in the initial wake-up phase, the power build-up process, and actual propagation anomalies. While changing the power frame by frame can expand the observation range, it requires multiple transmissions and waiting periods. The above-mentioned detection excitation incorporates weak signal acquisition, power increment, and multi-subcarrier training into a single continuous transmission. As a result, the receiver can record the gain transient using the power stage boundaries at the same wake-up time and immediately obtain frequency domain training data after the ramp-up is complete, reducing the interference of channel changes between different detection frames on the feature correspondence.

[0032] When the intelligent fusion terminal in the cable trench periodically wakes up to upload monitoring data, the preamble ramp can be divided into 5 power phases, each lasting 2 symbols. With a receiver sensitivity lower limit of -90 dBm and a preset power margin of 2 dB, the initial transmit power is set to -92 dBm. After determining the termination transmit power based on path loss and the receiver's maximum allowable input power, the low-power phase increases with smaller increments, while the high-power phase increases the increments. The receiver records the complete response from the appearance of a weak signal to the arrival of the training sequence within the same detection excitation. Even if the RF channel is not fully stable at wake-up time, S20 can still track gain changes according to the phase boundaries, and S30 can obtain corresponding subcarrier data from subsequent training sequences.

[0033] Step S20: Based on the detection response signal, an automatic gain control transient recording mechanism is used to perform a gain adjustment feature extraction task and output automatic gain control dynamic feature information; The automatic gain control (AGC) dynamic characteristic information describes the adjustment process and degree of abnormality of the AGC in each power stage when the receiver transitions from a low-power listening state to a probe signal receiving state. Before the probe response signal arrives, the receiver maintains low-power listening; after detecting the start position of the probe response signal, a gain transient recording window is established starting from that position. The recording window corresponds to the holding time of the preamble power ramp transmit segment in S10, ensuring that each gain switch falls within a defined power stage.

[0034] The recording window is divided into multiple power stages based on the transmit power changes corresponding to the probe response signal. The receiver counts the number of gain switches performed by the automatic gain control unit within each stage and saves the gain value at the end of that stage. The stage termination gain reflects the gain level that the receiver has converged to at the end of that input power, while the number of gain adjustments reflects the degree of repetition before reaching that level. Both types of records for each stage are saved in power stage order to avoid losing the dynamic evolution during the wake-up period by only retaining the final gain.

[0035] The preset maximum number of jitter cycles is based on the gain switching time required for the automatic gain control unit to complete one gain switch and the duration of a single power phase. The controller first calculates the ratio of the gain switching time to the phase duration, then rounds this ratio up to form a candidate number; if the candidate number is less than 1, it is set as one cycle; otherwise, the candidate number is used. When the actual number of adjustments for a power phase exceeds this upper limit, that phase is marked as a jitter phase. The resulting upper limit varies with the receiver switching speed and phase length, preventing the hardware-allowed normal switching from being mistakenly recorded as continuous oscillation.

[0036] The number of gain adjustments, the stage termination gain, and the number of jitter stages in each power stage sequentially form the automatic gain control transient feature vector. This vector is input into a pre-trained principal component analysis mapping function to obtain the automatic gain control anomaly mapping result, which is then normalized to 0 to 1 according to the mapping range determined in the training stage, forming the automatic gain control anomaly index AGCI. The feature vector and AGCI together constitute the automatic gain control dynamic feature information: the stage termination gain is used in S30 to eliminate the influence of the receive gain on the amplitude, the AGCI participates in anomaly fusion in S40, and the complete feature vector is used in S50 to predict the settling time of the communication impulse response.

[0037] Comparing only the received signal strength or the final gain of automatic gain control will treat monotonically converging gain and multiple repeated switching as the same result. For example, two probes may both eventually stop at the same gain, but one of them may have multiple switching back and forth in the middle stage, resulting in different levels of stability in subsequent data transmission. This step retains the number of adjustments within the stage, the stage termination value, and the number of jitter stages, and maps the multidimensional dynamic pattern to a fusionable AGCI, so that subsequent judgments can use a concise exponent without losing the original dynamic basis used for gain normalization and settling time prediction.

[0038] Assuming the receiver takes 0.6 microseconds to complete one gain switch and a single power phase lasts 2 microseconds, the ratio of these two phases is rounded up to obtain one cycle. Therefore, the preset maximum number of jitter cycles is 1. In a certain wake-up, the number of gain adjustments for the five power phases are 1, 3, 2, 1, and 0, respectively. The second and third phases are marked as jitter phases; the corresponding termination gains for these phases are 58 dB, 51 dB, 45 dB, 39 dB, and 36 dB, respectively. The above sequential data forms a transient feature vector, which, after processing by a pre-trained mapping function, yields an AGCI of 0.72. This result indicates that the anomaly originates not only from the final gain magnitude but also from the failure of intermediate phases to stabilize within the allowed number of switches.

[0039] Step S30: Based on the automatic gain control dynamic characteristic information and the detection response signal, a coherent channel estimation and subband discrete analysis mechanism is used to perform frequency domain preprocessing tasks and output the frequency domain discreteness index FCDI. The Frequency Domain Dispersion Index (FCDI) characterizes the amplitude fluctuation of the probe response signal within a local continuous frequency band after the gain effect is eliminated. The receiver first performs symbol synchronization and subcarrier demodulation on the probe response signal to determine the symbol boundaries of the multi-subcarrier training sequence and extracts multiple subcarrier received symbols. Since the training symbols corresponding to each subcarrier are pre-defined non-zero values, each received symbol can be divided by its corresponding training symbol to obtain the estimated channel frequency response value for that subcarrier.

[0040] The channel frequency response estimation still includes amplitude scaling caused by receiver automatic gain changes, therefore it needs to be normalized using the stage termination gain output from S20. The receiver converts the logarithmic stage termination gain into a linear amplitude gain based on the hardware gain calibration relationship, and then matches the corresponding gain according to the power stage of the subcarrier received symbol. The amplitude of each channel frequency response estimate is divided by the matched linear amplitude gain to form the gain-normalized amplitude. After this processing, the amplitude of the same subcarrier mainly reflects the propagation channel and local interference, rather than the gain adjustment made by the receiver to adapt to the input power.

[0041] The width of the continuous subband is determined by the root mean square delay spread of the intelligent converged terminal deployment environment. This delay spread can be obtained by measuring the multipath arrival time distribution using known training signals during deployment or operation and maintenance calibration, and is used to determine the coherence bandwidth. The controller divides each subcarrier into non-overlapping continuous subbands according to the condition that the frequency span of each subband is no greater than half of the coherence bandwidth. This restriction ensures that subcarriers within the same subband are in comparable local frequency ranges, and also avoids placing subcarriers that are far apart and have significantly different propagation characteristics into the same statistical unit.

[0042] For each continuous sub-band, the mean and variance of the gain normalization amplitude are calculated. The sub-band dispersion is obtained by dividing the variance by the sum of the square of the mean and a preset regularization constant. The regularization constant is positive and is used to limit abnormal amplification when local deep fading causes the mean to approach zero. The controller compares the dispersion of all continuous sub-bands and selects the maximum value as the FCDI output. Selecting the maximum value preserves the state of the most severely damaged sub-band and prevents it from losing its discriminative function after being averaged by other flat sub-bands.

[0043] The average received power across the entire band is not sensitive to deep fading of a small number of adjacent subcarriers, and directly calculating the variance of the unnormalized amplitude would misinterpret the phase switching of automatic gain control as channel fluctuations. This step first uses the dynamic characteristics of automatic gain control to complete amplitude normalization, then constrains the local statistical range with coherent bandwidth, and finally uses the regularized maximum subband dispersion to describe the most unfavorable frequency band. Thus, the FCDI received by S40 and AGCI form two types of evidence with different physical meanings: the former focuses on the local structure in the frequency domain, while the latter focuses on receiver gain transients.

[0044] In a probe training sequence containing 64 effective subcarriers, if the deployment environment calibration results allow it to be divided into 8 non-overlapping continuous sub-bands, the receiver calculates the gain normalization amplitude for each sub-band separately. In a single probe, the mean amplitude of the 3rd sub-band is 0.45, and the variance is 0.09. After adding a preset regularization constant, the sub-band dispersion is approximately 0.43. The dispersion of the remaining sub-bands is all below 0.08, so the FCDI is set to 0.43. Even if the average received power across the entire frequency band is still within the normal range, this indicator will still carry the concentrated anomalies of the 3rd sub-band into S40 to prevent local narrowband interference from being masked by the overall mean.

[0045] Step S40: Based on the automatic gain control dynamic feature information and the frequency domain dispersion index FCDI, a weighted fusion decision mechanism is used to perform the link availability determination task and output the link status determination result; The link status determination result consists of a link status identifier and a link anomaly judgment value, used to indicate whether the current link is available or abnormal, and retaining the continuous quantization degree that led to this conclusion. The controller extracts the AGCI from the automatic gain control dynamic characteristic information and normalizes the FCDI using a preset frequency domain dispersion reference value. In specific processing, the ratio of FCDI to the reference value is limited to a range not exceeding one, resulting in the normalized frequency domain dispersion index. The reference value is a positive number and can be predetermined based on the dispersion distribution of confirmed normal and abnormal link samples.

[0046] The normalized frequency domain dispersion index and AGCI are weighted and fused according to their respective weights. Both the frequency domain dispersion weight and the automatic gain control anomaly weight are not less than zero, and their sum is one, ensuring that the obtained link anomaly judgment value remains on a comparable and uniform scale. These two weights do not change arbitrarily in each run, but are determined based on the receiver operating characteristic curve analysis results of the training samples; by comparing the detection rate and false alarm rate under different weight combinations, a combination that meets the deployment requirements can be selected.

[0047] The controller compares the link anomaly judgment value with a preset anomaly judgment threshold. When the judgment value is less than the threshold, the link status flag is set to zero, indicating that the link is available; when the judgment value is greater than or equal to the threshold, the flag is set to one, indicating that the link is abnormal. The flag is used by the S50 to select whether to generate a delayed re-probe sub-instruction, while the judgment value continues to participate in the block error rate feedback condition and maintenance cycle parameter update. Retaining both can prevent subsequent control from only knowing whether an anomaly has occurred, but being unable to trace the degree of the anomaly.

[0048] An increase in AGCI may originate from repeated gain switching at the receiver, while an increase in FCDI indicates strong amplitude dispersion in at least one continuous subband. Weighted fusion allows one type of evidence to influence the decision even when the other type of evidence is weak, and also allows the superposition of two mild anomalies to exceed the threshold. Therefore, S40 does not rigidly threshold the two indicators separately, nor does it simply select the larger one, but rather forms a comprehensive decision value that can be calibrated from training samples on a unified normalized scale.

[0049] A single received signal strength threshold is prone to misjudgment when the overall power is normal but local frequency bands are damaged, and may also misjudge when the receiver briefly adjusts the gain but the channel is actually flat. Relying on multi-frame block error rate statistics requires several formal transmissions first. This step directly uses the gain transient and frequency domain discrete information already obtained in a single probe response to output the link status before formal data transmission, and feeds the continuous decision values ​​to the S50 to form a closed-loop feedback, thereby shortening the anomaly identification chain, while retaining a quantitative basis for subsequent threshold correction.

[0050] For example, if the AGCI output by S20 is 0.72, the FCDI output by S30 is 0.43, and the preset frequency domain dispersion reference value is 0.50, then the normalized frequency domain dispersion index is 0.86. If the frequency domain dispersion weight is 0.55 and the automatic gain control anomaly weight is 0.45, the weighted fusion result in a link anomaly judgment value of approximately 0.80. When the preset anomaly judgment threshold is 0.60, the link status flag is set to one. This result indicates that the current anomaly is not determined by a single gain jitter, but rather by a combination of gain transients and local frequency domain dispersion. Based on this, S50 proceeds to delay re-probe processing.

[0051] Step S50: Based on the link status determination result and the automatic gain control dynamic characteristic information, an adaptive anomaly handling mechanism is used to execute the transmission control task and output the transmission control command.

[0052] Transmission control commands are used to convert the link status conclusions of S40 into currently executable actions such as reprobe waiting, decision threshold adjustment, and maintenance parameter update. When the link status indicator represents an anomaly, the controller inputs the dynamic characteristic information of automatic gain control into a pre-trained regression model to obtain the steady-state time constant of the communication impulse response. The regression model uses the number of gain adjustments, phase termination gain, and number of jitter phases in the confirmed link status samples as input, and uses the actual observed response stabilization time as the training objective to make the prediction time correspond to the transient of the current round of automatic gain control.

[0053] The stabilization time constant also needs to be constrained within the allowable waiting range. When the predicted value is less than the preset minimum waiting time, the delayed reprobe waiting time is set according to the minimum waiting time; when the predicted value is greater than the preset maximum waiting time, it is set according to the maximum waiting time; when the predicted value is between the two, the predicted value is used directly. The controller generates a sub-instruction containing the delayed reprobe waiting time accordingly. The lower limit prevents premature reprobes from falling within the same impulse response period, while the upper limit prevents abnormal predicted values ​​from causing the terminal to stop transmitting for an extended period.

[0054] Threshold adjustment is triggered by actual transmission error results. The controller records the block error rate of each data transmission within a preset statistical window and calculates the window average. Only when this average is higher than the preset target block error rate, and the link anomaly judgment value corresponding to each transmission within the window is lower than the current anomaly judgment threshold, is continuous under-detection considered to exist. At this time, the current threshold is multiplied by a preset threshold reduction factor greater than zero and less than 1 to obtain the updated anomaly judgment threshold and generate a threshold adjustment sub-instruction. If a sample that has been judged as an anomaly has appeared within the window, the increase in block error may have been interpreted by the detected link, and the threshold will not be tightened again based on this.

[0055] At the end of each pre-defined deployment and maintenance cycle, the controller collects samples of the confirmed link status within that cycle and uses these samples to update the stable time regression model, frequency domain dispersion weights, automatic gain control anomaly weights, and anomaly decision thresholds. Confirmed samples can be verified by re-probe results or subsequent transmission error block results, ensuring that the model and fusion parameters gradually correspond to the current deployment environment. This periodic update replaces the old approach of recalibrating only when the deployment location changes, covering situations where the location remains unchanged but the electromagnetic environment changes slowly, and is consistent with the periodic update conditions in the final claims.

[0056] The delayed reprobe sub-instruction, threshold adjustment sub-instruction, and decision parameter update results are combined into a transmission control instruction based on the actual triggering situation in the current round; if none of the three are generated, an instruction to maintain the current data transmission state is output. In this way, transient anomalies are handled by the waiting time, long-term under-detection is handled by threshold adjustment, and changes in statistical characteristics are handled by the maintenance cycle update; the three time scales do not substitute for each other. Compared to fixed waiting and fixed thresholds, the control action can correspond to anomaly recovery time, actual error block performance, and changes in deployment cycle samples, reducing the occurrence of continuous premature reprobes or long-term use of mismatched parameters.

[0057] After a wake-up detection in an industrial field was flagged as abnormal, the regression model predicted a settling time of 3.2 milliseconds based on the number of gain adjustments and jitter phases. The preset minimum wait time was 1 millisecond, and the maximum wait time was 10 milliseconds; therefore, the delay re-detection wait time was set at 3.2 milliseconds. Subsequently, within a statistical window containing 10 data transmissions, the average block error rate reached 11%, exceeding the target value of 8%, and the link anomaly judgment values ​​corresponding to the 10 transmissions were all below the current threshold of 0.60. With a threshold reduction factor of 0.9, the updated threshold was 0.54. After the maintenance cycle ended, the confirmed link samples from that cycle were used to update the regression model and the two fusion weights, ensuring that the wait prediction and link determination for the next cycle could incorporate the data from this field operation.

[0058] Example 2: Furthermore, the present invention provides a data transmission anomaly detection system for intelligent converged terminals, employing a data transmission anomaly detection method for intelligent converged terminals as described in the above embodiments, which can solve a technical problem related to data transmission anomaly detection in intelligent converged terminals. The beneficial effects of the data transmission anomaly detection system for intelligent converged terminals provided by the present invention are the same as those of the data transmission anomaly detection method for intelligent converged terminals provided in the above embodiments, and other technical features of the data transmission anomaly detection system for intelligent converged terminals are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0059] Example 3: This invention provides a data transmission anomaly detection device for a smart converged terminal. The device includes at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the processor, which are then executed to enable the processor to perform the data transmission anomaly detection method for a smart converged terminal described in Example 1. This data transmission anomaly detection device for a smart converged terminal, as described in this embodiment, may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This data transmission anomaly detection device for a smart converged terminal is merely an example and should not be construed as limiting the functionality or scope of the invention. The device may also include a processing unit (e.g., a central processing unit, a graphics processing unit), which can perform various appropriate actions and processes based on a program stored in a read-only memory or a program loaded from a storage device into a random access memory. The random access memory also stores various programs and data required for the operation of a data transmission anomaly detection device for a smart converged terminal. The processing unit, read-only memory, and random access memory are interconnected via a bus. The I / O interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the data transmission anomaly detection device for a smart converged terminal to exchange data with other devices wirelessly or via wired communication. While a data transmission anomaly detection device for a smart converged terminal with various systems has been described, it should be understood that implementation or possession of all the described systems is not required. More or fewer systems can be implemented alternatively.

[0060] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data transmission anomaly detection method for a smart converged terminal as described above. The computer program product provided by this invention can solve the technical problem of data transmission anomaly detection for a smart converged terminal. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the data transmission anomaly detection method for a smart converged terminal provided in the above embodiments, and will not be repeated here.

[0061] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0062] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0063] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for detecting data transmission anomalies in intelligent converged terminals, characterized in that, The method includes: Step S10: Obtain the terminal sleep / wake-up state of the intelligent fusion terminal, and based on the terminal sleep / wake-up state, execute the detection-triggered transmission task using a preset nonlinear power ramp transmission mechanism, and output the detection response signal; Step S20: Based on the detection response signal, an automatic gain control transient recording mechanism is used to perform a gain adjustment feature extraction task and output automatic gain control dynamic feature information; Step S30: Based on the automatic gain control dynamic characteristic information and the detection response signal, a coherent channel estimation and subband discrete analysis mechanism is used to perform frequency domain preprocessing tasks and output the frequency domain discreteness index FCDI. Step S40: Based on the automatic gain control dynamic feature information and the frequency domain dispersion index FCDI, a weighted fusion decision mechanism is used to perform the link availability determination task and output the link status determination result; Step S50: Based on the link status determination result and the automatic gain control dynamic characteristic information, an adaptive anomaly handling mechanism is used to execute the transmission control task and output the transmission control command.

2. The data transmission anomaly detection method for a smart fusion terminal as described in claim 1, characterized in that, Step S10, which involves obtaining the terminal sleep / wake-up state of the intelligent fusion terminal, executing a detection-triggered transmission task based on the terminal sleep / wake-up state using a preset nonlinear power ramp transmission mechanism, and outputting a detection response signal, specifically includes: Step S101: When the terminal sleep-wake state characterizes the intelligent fusion terminal switching from sleep state to wake state, a detection excitation is generated that sequentially includes a preamble power ramp transmission segment, a multi-subcarrier training sequence, and a silent protection interval. Step S102: Divide the preamble power ramp transmission segment into at least three sequentially arranged power stages with the same symbol duration. Set the transmission power of each power stage in a non-uniform increment manner, so that the transmission power difference between adjacent power stages below the preset power threshold is less than the transmission power difference between adjacent power stages. Determine the difference between the receiver sensitivity lower limit and the preset power margin as the initial transmission power, and set the termination transmission power according to the condition that the receiver input power corresponding to the termination power stage does not exceed the maximum allowable input power of the receiver, thus obtaining the preamble power ramp transmission segment. Step S103: After the preamble power ramp transmission segment, the multi-subcarrier training sequence is transmitted, and multiple non-zero training symbols are modulated onto the corresponding subcarriers. After the multi-subcarrier training sequence, the silence protection interval is set, and the receiving end collects the received data corresponding to the detection excitation to form the detection response signal.

3. The data transmission anomaly detection method for a smart fusion terminal as described in claim 1, characterized in that, Step S20, which involves performing gain adjustment feature extraction based on the detection response signal using an automatic gain control transient recording mechanism and outputting automatic gain control dynamic feature information, specifically includes: Step S201: Before the detection response signal arrives at the receiving end, the receiving end is configured to a low-power listening state, and when the starting position of the detection response signal is detected, a gain transient recording window corresponding to the detection response signal is established; Step S202: According to the transmit power change corresponding to the detection response signal, the gain transient recording window is divided into multiple power stages, and the number of gain adjustments and the stage termination gain of the automatic gain control unit at the receiving end in each power stage are recorded respectively; the power stage where the number of gain adjustments exceeds the preset maximum jitter number is marked as a jitter stage, the number of jitter stages is counted, and the number of gain adjustments, the stage termination gain and the number of jitter stages are arranged according to the order of each power stage to obtain the automatic gain control transient feature vector; Step S203: Input the automatic gain control transient feature vector into the pre-trained principal component analysis mapping function to obtain the automatic gain control anomaly mapping result. Normalize the automatic gain control anomaly mapping result to the value range of 0 to 1 to obtain the automatic gain control anomaly index AGCI. Combine the automatic gain control transient feature vector and the automatic gain control anomaly index AGCI to form the automatic gain control dynamic feature information.

4. The data transmission anomaly detection method for a smart fusion terminal as described in claim 3, characterized in that, In step S202, the process of setting the preset maximum number of jitters includes: Obtain the gain switching time required for the automatic gain control unit to complete one gain switching, and obtain the stage duration corresponding to a single power stage; Calculate the ratio of the gain switching time to the stage duration, and round the ratio up to obtain the candidate jitter count; When the number of candidate jitters is less than 1, the preset maximum number of jitters is set to 1; when the number of candidate jitters is greater than or equal to 1, the preset maximum number of jitters is set to the number of candidate jitters.

5. The data transmission anomaly detection method for a smart fusion terminal as described in claim 3, characterized in that, Step S30, based on the automatic gain control dynamic characteristic information and the detection response signal, employs a coherent channel estimation and subband discrete analysis mechanism to perform frequency domain preprocessing and output the frequency domain dispersion index FCDI. This step specifically includes: Step S301: Perform symbol synchronization and subcarrier demodulation on the detection response signal, extract multiple subcarrier received symbols, and obtain the preset non-zero training symbols corresponding to each subcarrier received symbol; divide each subcarrier received symbol by the corresponding preset non-zero training symbol to obtain the channel frequency response estimate value corresponding to each subcarrier. Step S302: Extract the stage termination gain corresponding to each power stage from the automatic gain control dynamic feature information, convert the stage termination gain into a linear amplitude gain, and determine the linear amplitude gain corresponding to each subcarrier according to the power stage to which each subcarrier received symbol belongs; divide the amplitude of each channel frequency response estimate by the corresponding linear amplitude gain to obtain the gain normalization amplitude of each subcarrier; obtain the root mean square delay spread of the deployment environment where the intelligent fusion terminal is located, determine the coherent bandwidth according to the root mean square delay spread, divide each subcarrier into multiple non-overlapping continuous sub-bands according to the condition that the frequency span of each sub-band is no greater than half of the coherent bandwidth, and calculate the mean and variance of the gain normalization amplitude in each continuous sub-band respectively; Step S303: For each continuous sub-band, divide the variance of the gain normalization amplitude within the continuous sub-band by the sum of the square of the mean of the gain normalization amplitude within the continuous sub-band and the preset regularization constant to obtain the corresponding sub-band dispersion; select the maximum value from the sub-band dispersion corresponding to each continuous sub-band as the frequency domain dispersion index FCDI output, wherein the preset regularization constant is a positive number.

6. The data transmission anomaly detection method for a smart fusion terminal as described in claim 1, characterized in that, Step S40, which involves performing a link availability determination task based on the automatic gain control dynamic characteristic information and the frequency domain dispersion index FCDI, and outputting the link status determination result using a weighted fusion decision mechanism, specifically includes: Step S401: Extract the Automatic Gain Control Anomaly Index (AGCI) from the automatic gain control dynamic feature information, and normalize the frequency domain dispersion index (FCDI) according to a preset frequency domain dispersion reference value to obtain the normalized frequency domain dispersion index: in, The normalized frequency domain dispersion index is... The preset frequency domain dispersion reference value is a positive number. Step S402: Perform weighted fusion of the normalized frequency domain dispersion index and the automatic gain control anomaly index AGCI to obtain the link anomaly judgment value: in, This is the link anomaly judgment value. For frequency domain dispersion weights, For automatic gain control abnormal weights, , ,and The frequency domain dispersion weight and the automatic gain control anomaly weight are determined based on the receiver operating characteristic curve analysis results of the training samples. Step S403: Compare the link anomaly judgment value with the preset anomaly judgment threshold, and determine the link status identifier according to the following formula: in, This is the link status identifier. A preset anomaly judgment threshold is set; when the link status identifier is zero, it indicates that the link is available; when the link status identifier is one, it indicates that the link is abnormal; the link status identifier and the link anomaly judgment value are combined to form the link status judgment result.

7. The data transmission anomaly detection method for a smart fusion terminal as described in claim 6, characterized in that, Step S50, which involves executing a transmission control task and outputting a transmission control command based on the link state determination result and the automatic gain control dynamic characteristic information, specifically includes: Step S501: Extract the link status identifier from the link status determination result. When the link status identifier indicates a link anomaly, input the automatic gain control dynamic feature information into a pre-trained regression model to obtain the stable time constant of the communication impulse response. When the stable time constant is less than a preset minimum waiting time, set the delay reprobe waiting time to the preset minimum waiting time. When the stable time constant is greater than a preset maximum waiting time, set the delay reprobe waiting time to the preset maximum waiting time. When the stable time constant is between the preset minimum waiting time and the preset maximum waiting time, set the delay reprobe waiting time to the stable time constant and generate a delay reprobe sub-instruction containing the delay reprobe waiting time. Step S502: Obtain the block error rate corresponding to each data transmission within a preset statistical window, and calculate the window average block error rate corresponding to the preset statistical window; when the window average block error rate is greater than a preset target block error rate, And when the link anomaly judgment value corresponding to each data transmission within the preset statistical window is less than the preset anomaly judgment threshold, The preset anomaly judgment threshold is multiplied by the preset threshold reduction factor to obtain the updated anomaly judgment threshold, and a threshold adjustment sub-instruction containing the updated anomaly judgment threshold is generated, wherein the preset threshold reduction factor is greater than zero and less than 1. Step S503: At the end of each preset deployment and maintenance cycle, obtain samples of confirmed link status within the preset deployment and maintenance cycle, and use the samples to update the regression model, the frequency domain dispersion weight, the automatic gain control anomaly weight, and the preset anomaly decision threshold to obtain the decision parameter update result; generate the transmission control instruction based on the delay reprobe sub-instruction, the threshold adjustment sub-instruction, and the decision parameter update result; if the delay reprobe sub-instruction, the threshold adjustment sub-instruction, and the decision parameter update result are not generated, generate a transmission control instruction to maintain the current data transmission status.

8. A data transmission anomaly detection system for a smart converged terminal, applied to the data transmission anomaly detection method for a smart converged terminal according to any one of claims 1 to 7, characterized in that, The data transmission anomaly detection system for intelligent converged terminals includes: The detection trigger module is used to obtain the terminal sleep / wake-up state of the intelligent fusion terminal, and to perform the detection trigger transmission task based on the terminal sleep / wake-up state using a preset nonlinear power ramp transmission mechanism, and output the detection response signal. The transient feature extraction module is used to perform gain adjustment feature extraction tasks based on the detection response signal using an automatic gain control transient recording mechanism, and output automatic gain control dynamic feature information; The frequency domain preprocessing module is used to perform frequency domain preprocessing tasks based on the automatic gain control dynamic characteristic information and the detection response signal, using a coherent channel estimation and subband discrete analysis mechanism, and outputs the frequency domain discreteness index FCDI. The fusion decision module is used to perform a link availability determination task based on the automatic gain control dynamic feature information and the frequency domain dispersion index FCDI, and output the link status determination result by adopting a weighted fusion decision mechanism. The anomaly handling module is used to execute transmission control tasks and output transmission control commands by adopting an adaptive anomaly handling mechanism based on the link status determination result and the automatic gain control dynamic characteristic information.

9. A data transmission anomaly detection device for intelligent fusion terminals, characterized in that, The data transmission anomaly detection device for a smart converged terminal includes: a memory, a processor, and a data transmission anomaly detection program for a smart converged terminal stored in the memory and executable on the processor. When the data transmission anomaly detection program for a smart converged terminal is executed by the processor, it implements a data transmission anomaly detection method for a smart converged terminal according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a data transmission anomaly detection program for a smart converged terminal, which, when executed by a processor, implements a data transmission anomaly detection method for a smart converged terminal according to any one of claims 1 to 7.

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