HPLC (High Performance Liquid Chromatography) anti-interference and dual-mode switching method and system based on channel state prediction

Abstract

The invention belongs to the technical field of carrier communication, and discloses an HPLC (High Performance Liquid Chromatography) anti-interference and dual-mode switching method and system based on channel state prediction, and the method comprises the steps: firstly, based on HPLC frame-level communication data, calculating frame-level statistical characteristics such as a frame bit error rate and average retransmission times, and constructing a time sequence; predicting the deterioration probability of a carrier channel based on the time sequence, and synchronously evaluating the prediction credibility; and finally, according to the deterioration probability and the prediction credibility, controlling the communication system to migrate among three communication states of carrier enhancement, double-link parallel and wireless primary use. The method can identify the channel degradation trend in advance, reduces frequent switching, improves the stability and reliability of a communication system, adapts to the existing dual-mode communication equipment, and has a remarkable practical value.

Description

Technical Field

[0001] This application relates to the field of carrier communication technology, and more specifically, to a method and system for HPLC anti-interference and dual-mode switching based on channel state prediction. Background Technology

[0002] In scenarios such as user-side communication in low-voltage distribution networks and communication between distribution concentrators and smart meters, dual-mode communication systems combining high-speed power line carrier communication (HPLC) and high-frequency wireless communication (HRF) are widely used due to their advantages such as convenient deployment and comprehensive coverage.

[0003] However, existing dual-mode communication systems face numerous technical bottlenecks in actual operation. Carrier channels are susceptible to interference from switching power supplies, motor loads, frequency converters, etc., and their degradation typically exhibits a gradual evolutionary pattern, manifested as a gradual increase in frame error rate, an increase in the number of retransmissions, and aggravated frame arrival time jitter. However, existing technologies mostly rely on the current bit error rate or communication interruption status for judgment, making it difficult to identify this degradation trend in advance, resulting in a lag in anti-interference measures.

[0004] Meanwhile, the switching decision for dual-mode communication lacks a scientific basis. Existing solutions often use fixed thresholds or instantaneous channel quality indicators to trigger the switching, which can easily misjudge short-term, sudden interference as continuous channel degradation, causing frequent round-trip switching between the carrier and the wireless link, significantly increasing communication latency and system power consumption, and reducing communication continuity and stability.

[0005] Furthermore, the reliability of the prediction results was not quantitatively assessed, and it was impossible to distinguish between instantaneous fluctuations and continuous trends. This made it difficult for the prediction results to effectively guide the gradual adjustment of communication modes. The lack of a unified control mechanism to coordinate the channel deterioration trend, prediction reliability, and dual-mode switching process restricted the improvement of the overall performance of the communication system. Summary of the Invention

[0006] In response, this application provides a method and system for HPLC anti-interference and dual-mode switching based on channel state prediction, so as to at least partially solve the above-mentioned technical problems.

[0007] This application provides a method for HPLC anti-interference and dual-mode switching based on channel state prediction, applicable to a dual-mode communication system of power line carrier communication and wireless communication, including the following method steps: Based on HPLC frame-level communication data, frame-level statistical features are calculated and a time series of frame-level statistical features is constructed. The frame-level statistical features include at least frame error rate, average number of retransmissions, frame arrival time jitter, and acknowledgment ratio. Based on the frame-level statistical feature time series, the degradation probability of the carrier channel is predicted; The reliability of the degradation prediction results is assessed based on the predicted degradation probability. Based on the degradation probability and the prediction confidence, the control communication system performs state transitions between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state.

[0008] In one possible embodiment, the HPLC frame-level communication data includes at least: the total number of HPLC frames sent, the number of CRC check failure frames, the number of retransmitted frames, the number of ACK frames, the number of NACK frames, and the HPLC frame reception timestamp.

[0009] In one possible embodiment, calculating frame-level statistical features includes at least: calculating the frame error rate based on the number of CRC check failure frames and the number of sent HPLC frames; calculating the average number of retransmissions based on the number of retransmitted frames and the number of successful frames; calculating the frame arrival time jitter based on the time interval between the timestamps of adjacent HPLC frames; and calculating the acknowledgment ratio based on the number of ACK frames and the total number of acknowledgment-related frames.

[0010] In one possible embodiment, the predicted carrier channel degradation probability specifically involves inputting the normalized frame-level statistical feature time series as an observation sequence into a pre-trained Hidden Markov Model to obtain the corresponding optimal channel state sequence, and calculating the predicted channel degradation probability of the carrier channel in subsequent communication cycles based on the state transition relationship of the Hidden Markov Model.

[0011] In one possible embodiment, the hidden Markov model includes at least three channel states, corresponding to the normal state, slightly degraded state, and severely degraded state of the carrier channel, respectively, and the frame-level statistical features are used as observations to establish a probabilistic correlation with the channel states.

[0012] In one possible embodiment, the prediction reliability assessment is based on at least one of the following metrics: the magnitude of change in the degradation probability over multiple consecutive prediction periods, the consistency of the degradation probability under different statistical windows, and the number of times the degradation probability shows a prediction reversal in a short period of time.

[0013] In one possible embodiment, controlling the communication system to perform state transitions between a carrier-enhanced communication state, a dual-link parallel communication state, and a wireless primary communication state specifically includes: maintaining the carrier-enhanced communication state when the degradation probability is lower than a first preset threshold, or when the degradation probability is higher than the first preset threshold but the prediction confidence is lower than a first confidence threshold; transitioning from the carrier-enhanced communication state to the dual-link parallel communication state when the degradation probability is higher than a second preset threshold, or when the degradation probability is higher than the first preset threshold and the prediction confidence is higher than the first confidence threshold; and transitioning from the dual-link parallel communication state to the wireless primary communication state when the degradation probability is higher than a third preset threshold and the prediction confidence is higher than the second confidence threshold.

[0014] In another aspect, this application also provides a system for HPLC anti-interference and dual-mode switching based on channel state prediction, comprising: The statistical feature construction module is used to calculate frame-level statistical features and construct a frame-level statistical feature time series based on HPLC frame-level communication data. The frame-level statistical features include at least frame error rate, average number of retransmissions, frame arrival time jitter, and acknowledgment ratio. The degradation probability prediction module is used to predict the degradation probability of the carrier channel based on the frame-level statistical feature time series. The prediction reliability assessment module is used to assess the prediction reliability of the degradation prediction result based on the predicted degradation probability. The state transition control module is used to control the communication system to perform state transitions between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state based on the degradation probability and the prediction confidence.

[0015] This application also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the HPLC anti-interference and dual-mode switching method based on channel state prediction as described above.

[0016] In another aspect, this application provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor to implement the HPLC anti-interference and dual-mode switching method based on channel state prediction as described above.

[0017] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the HPLC anti-interference and dual-mode switching method based on channel state prediction as described above.

[0018] This application presents a complete anti-interference solution for HPLC carrier communication, achieved through HPLC frame-level statistical feature calculation and time series construction, hidden Markov model-driven channel degradation probability prediction, multi-dimensional prediction reliability assessment, and two-parameter-based progressive state transition control. It can identify carrier channel degradation trends in advance, avoiding sudden communication interruptions; by constraining switching decisions with prediction reliability, it effectively reduces frequent switching caused by short-term interference, lowering communication jitter; progressive state transition ensures precise matching between communication mode adjustments and channel state evolution, improving system stability and reliability; furthermore, the method is implemented based on existing HPLC protocols and communication modules, resulting in low cost, strong adaptability, and applicability to existing HPLC / HRF dual-mode communication equipment through software upgrades, demonstrating significant practical value in complex interference scenarios such as low-voltage power distribution networks. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of a method for HPLC anti-interference and dual-mode switching based on channel state prediction, provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of the degradation probability prediction process provided in an embodiment of this application.

[0022] Figure 3 This is a schematic diagram of the calculation and prediction confidence process provided in the embodiments of this application.

[0023] Figure 4 This is a schematic diagram of the structure of a system for HPLC anti-interference and dual-mode switching based on channel state prediction provided in an embodiment of this application.

[0024] Figure 5 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that all user information (including but not limited to user device information, user personal information, object information corresponding to device usage data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, device usage data, etc.) involved in all embodiments of this disclosure are information and data authorized by the user or fully authorized by all parties.

[0027] This application applies to dual-mode communication systems using high-speed power line carrier communication (HPLC) and high-frequency wireless communication (HRF). Typical application scenarios include communication systems between user-side communication terminals in low-voltage distribution networks and between distribution area concentrators, as well as communication terminals or concentrators with dual communication interfaces. The executing entity can be a communication device or system node integrating a high-speed power line carrier communication (HPLC) module, a high-speed wireless communication (HRF) module, and a communication control and scheduling module. This method is triggered when the communication terminal or concentrator is powered on or when the communication control module is activated.

[0028] The following detailed description, in conjunction with specific embodiments, illustrates the implementation process of the HPLC anti-interference and dual-mode switching method based on channel state prediction described in this application. It should be noted that these embodiments are merely for explaining this application and not for limiting its scope of protection. Any conventional adjustments or substitutions made by those skilled in the art to the steps without departing from the concept of this application should be included within the scope of protection of this application.

[0029] like Figure 1 As shown in the figure, this application discloses a schematic diagram of a method for HPLC anti-interference and dual-mode switching based on channel state prediction, including the following method steps: S1. Based on HPLC frame-level communication data, calculate frame-level statistical features and construct a frame-level statistical feature time series. The frame-level statistical features include at least frame error rate, average number of retransmissions, frame arrival time jitter, and acknowledgment ratio. S2. Based on the frame-level statistical feature time series, predict the degradation probability of the carrier channel; S3. Evaluate the reliability of the degradation prediction results based on the predicted degradation probability; S4. Based on the degradation probability and the prediction confidence, control the communication system to perform state transitions between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state.

[0030] In some embodiments, for step S1, the communication control module monitors the communication process in real time at the protocol layer of the HPLC protocol through a data interaction channel established with the protocol processing module of the HPLC protocol. It periodically collects HPLC frame-level communication data generated within a preset frame statistical time window, i.e., the frame structure data of the HPLC protocol. The acquisition process is carried out in parallel with the core operations of the HPLC protocol, such as frame encapsulation, transmission, and reception, without affecting the normal flow of communication services, ensuring the real-time nature of data acquisition and service continuity.

[0031] In one embodiment, the collected HPLC frame-level communication data includes at least the total number of HPLC frames sent, the number of CRC check failure frames, the number of retransmitted frames, the number of ACK frames, the number of NACK frames, and the HPLC frame reception timestamp.

[0032] Specifically, the total number of HPLC frames sent is the cumulative number of all frames sent to the communication link by the protocol processing module of the protocol used by HPLC within the current statistical time window; the number of CRC check failure frames is the number of frames whose CRC check results do not match after the receiver performs CRC check on the received frames; the number of retransmitted frames is the number of frames retransmitted by the sender because it did not receive an ACK frame or received a NACK frame; the number of ACK frames is the number of acknowledgment frames returned by the receiver after successfully receiving a frame; the number of NACK frames is the number of denial frames returned by the receiver when it failed to receive a frame or when there is a dispute about the frame content; the HPLC frame reception timestamp is the system time recorded by the receiver when it successfully captures each frame of data, and its time accuracy must meet the requirement of distinguishing the reception order of adjacent frames.

[0033] The collected data is organized in frames statistical time windows as the basic unit. Each time window corresponds to a set of records containing time interval identifiers (start time and end time) and specific values ​​of various frame-level communication data. All records are stored in the local storage unit of the communication control module in chronological order to form a structured data set, providing data support for subsequent statistical feature calculations.

[0034] After completing the data acquisition for each frame statistical time window, the raw frame-level communication data can be quantified and statistically analyzed to generate frame-level statistical features that can characterize the operating status of the carrier channel. The calculation process can meet the real-time requirements and avoid the impact of calculation delay on subsequent prediction and control processes.

[0035] In one embodiment, frame-level statistical feature calculation includes at least the following four core components: Frame error rate calculation: The frame error rate is determined by the ratio of the number of CRC check failure frames to the total number of HPLC frames sent. This characteristic directly reflects the probability of data errors during channel transmission. The calculation formula is as follows: in, Indicates the frame error rate. Indicates the number of frames that failed CRC check. This indicates the total number of HPLC frames sent.

[0036] Average retransmission count calculation: The average retransmission count is determined by the ratio of the number of retransmitted frames to the number of successful frames. The number of successful frames is the total number of HPLC frames sent minus the sum of the number of CRC check failure frames and the number of transmission timeout frames. This feature quantifies the impact of channel interference on communication efficiency, and its calculation formula is as follows: in, This represents the average number of retransmissions. Indicates the number of retransmitted frames. Indicates the number of successful frames.

[0037] Frame arrival time jitter calculation: Frame arrival time jitter is the statistical dispersion of the time interval between adjacent HPLC frame receptions. The calculation process consists of three steps: First, based on the HPLC frame reception timestamp, the reception time interval between two adjacent frames is calculated. ( For the first Frame reception timestamp, For the first (The frame's received timestamp); then calculate the average of all adjacent time intervals. Finally, the frame arrival time jitter is obtained by the root mean square of the deviation between each time interval and the average value. The calculation formula is as follows: in, This indicates frame arrival time jitter. This represents the total number of HPLC frames received within the current statistical time window.

[0038] Acknowledgment ratio calculation: The acknowledgment ratio is determined by the ratio of the number of ACK frames to the total number of acknowledgment-related frames. The total number of acknowledgment-related frames is the sum of the number of ACK frames and the number of NACK frames. This characteristic reflects the bidirectional interaction quality of the communication link, and its calculation formula is as follows: in, Indicates the confirmation ratio. Indicates the number of ACK frames. Indicates the number of NACK frames.

[0039] The calculated statistical characteristics, such as frame error rate, average retransmission count, frame arrival time jitter, and acknowledgment ratio, are stored sequentially in the storage unit of the communication control module, forming a frame-level statistical feature time series. This time series uses the frame statistical time window as its time dimension, with each time point corresponding to a complete set of statistical feature values, continuously reflecting the evolution of the carrier channel state. The storage process employs a cyclic overwrite mechanism; when the storage capacity reaches its limit, the earliest statistical feature data is automatically overwritten, ensuring sufficient recent data is retained within limited storage resources, providing continuous and complete input data for subsequent channel degradation probability prediction.

[0040] In some embodiments, step S2, carrier channel degradation probability prediction, addresses the technical problem of delayed carrier channel interference identification in existing technologies. Existing technologies typically judge channel quality based solely on the current bit error rate or communication interruption status, failing to anticipate channel degradation trends. This step, however, uses the analysis of frame-level statistical feature time series and a hidden Markov model to predict future channel states, enabling early prediction before significant channel performance degradation and allowing sufficient time for communication mode adjustments. The principle is that carrier channel interference exhibits significant temporal correlation and evolution; continuous changes in frame-level statistical features reflect gradual channel state trends. By establishing a mapping relationship between feature changes and channel degradation, probabilistic prediction of future channel states can be achieved.

[0041] In one embodiment, this application uses a Hidden Markov Model (HMM) as the prediction model. This model has a simple structure and low computational cost, and is suitable for resource-constrained scenarios in embedded communication devices. It can reduce the system's computing power consumption while ensuring prediction accuracy.

[0042] The core structure of a Hidden Markov Model includes a set of states, a set of observations, a state transition probability matrix, an observation probability matrix, and an initial state probability vector, which are defined as follows: State set :in The corresponding carrier channel in its normal state. For a slightly deteriorated state, For severely degraded conditions, this three-state classification comprehensively covers typical channel operating states and closely matches the changing patterns of frame-level statistical characteristics. The quantification basis for the state classification is as follows: when the frame bit error rate is in the low range, the average retransmission count is close to 0, the frame arrival time jitter is small, and the acknowledgment ratio is in the high range, it is determined to be... When all statistical characteristics are in the middle range, and the channel performance is slightly degraded but still meets basic communication requirements, it is determined to be... When the frame error rate increases significantly, the average retransmission count increases dramatically, frame arrival time jitter is severe, and the acknowledgment ratio decreases significantly, and channel performance seriously affects communication quality, it is determined to be... .

[0043] Observation set : Corresponds one-to-one with the frame-level statistical features calculated earlier, For frame error rate, This represents the average number of retransmissions. For frame arrival time jitter, To confirm the proportions, the elements of the observation set were used directly as input data for the model.

[0044] State transition probability matrix :in Indicates the current channel state. At that moment, the state transitions to the next moment. The probability, this matrix quantifies the transition patterns between channel states, for example This represents the probability of transitioning from a normal state to a slightly deteriorated state. This represents the probability of transitioning from a slightly deteriorated state to a severely deteriorated state.

[0045] Observation probability matrix :in Indicates the channel is in a state At that time, the observed features The probability of a specific value is given by this matrix, which establishes the probabilistic relationship between the hidden channel state and the observable frame-level statistical features. It is the core basis for the model to infer from the observed data to the hidden state.

[0046] Initial state probability vector :in This indicates the channel's state at the initial prediction time. The probability is given by the vector, which serves as the initial input condition for the model. Its value is determined based on the statistics of the initial channel state when the communication system starts.

[0047] In one embodiment, the Hidden Markov Model can be trained offline before actual deployment to determine the state transition probability matrix. Observation probability matrix and initial state probability vector The specific values ​​and the training process are as follows: Training data acquisition: A large amount of HPLC frame-level communication data is collected under different communication environments (including no interference, slight interference, and severe interference scenarios), and then the corresponding frame-level statistical feature time series is calculated. Each training sample contains a complete feature sequence of multiple consecutive frame statistical time windows. The number of samples must meet the statistical reliability requirements of model training to ensure coverage of various evolution scenarios of channel state.

[0048] Status Labeling: Based on the collected frame-level statistical feature data and combined with actual channel test results, such as channel attenuation and noise power parameters obtained through a dedicated channel tester, the channel state corresponding to each time window of each training sample is labeled to clarify its category. , or The annotation process follows preset state quantification standards to ensure the accuracy and consistency of the annotation results. For example, when the frame error rate is less than 0.01, the average retransmission count is less than 0.1, the frame arrival time jitter is less than a preset small threshold, and the acknowledgment ratio is higher than 0.95, it is labeled as... When the frame error rate is between 0.01 and 0.1, the average retransmission count is between 0.1 and 1, the frame arrival time jitter is between a small and medium threshold, and the acknowledgment ratio is between 0.8 and 0.95, it is marked as... When the frame error rate is higher than 0.1%, the average retransmission count is greater than 1, the frame arrival time jitter is greater than a preset threshold, and the acknowledgment ratio is lower than 0.8, it is marked as... .

[0049] Model parameter estimation: Optionally, the Baum-Welch algorithm is used to iteratively estimate the model parameters. This algorithm is based on the expectation-maximization (EM) principle and can automatically learn the model parameters from the observed sequence without prior knowledge of the state sequence. The specific steps are as follows: First, initialize the state transition probability matrix. Observation probability matrix and initial state probability vector The initial values ​​can be set based on experience or generated randomly. Next, the probability of the observation sequence under given model parameters is calculated using a forward algorithm, and the probability of being in a certain state at the current time step is calculated using a backward algorithm given the model parameters and the observation sequence. Based on the calculation results of the forward-backward algorithms, the state transition probability matrix is ​​updated. Observation probability matrix and initial state probability vector Repeat the above iterative process until the model parameters converge, i.e., the change in parameters is less than the preset convergence threshold, to obtain the final trained model parameters.

[0050] In one embodiment, carrier channel degradation probability prediction is achieved based on a trained Hidden Markov Model and a constructed frame-level statistical feature time series. (See also...) Figure 2 , Figure 2 This is a schematic diagram of the degradation probability prediction process provided in an embodiment of this application. The specific process is as follows: In S201, input data preprocessing is performed. The communication control module reads feature data from the most recent frame statistical time windows from the frame-level statistical feature time series to form a predicted input sequence. First, outlier detection and processing are performed on the input sequence. For feature values ​​that exceed reasonable ranges, such as frame bit error rates greater than 1 or less than 0, linear interpolation can be used for completion. For incomplete sequences caused by missing data, they are filled by extending the trend of adjacent valid data to ensure the integrity and validity of the input sequence.

[0051] In S202, feature normalization is performed. Since the dimensions and numerical ranges of statistical features at each frame level differ—for example, the frame error rate is between 0 and 1, and the average retransmission count can be any non-negative real number—normalization is performed on the preprocessed input sequence to eliminate the impact of these dimensional differences on the model's prediction accuracy. This embodiment uses the min-max normalization method to map each feature value to the [0,1] interval. The calculation formula is as follows: in, These are the normalized eigenvalues. These are the original eigenvalues. This is the minimum value of this feature in historical statistical data. This represents the maximum value of the feature in historical statistical data. Normalization ensures that each feature has a uniform numerical range, improving the model's convergence speed and prediction accuracy.

[0052] In S203, optimal state sequence reasoning is performed. The normalized feature sequence is used as the observation sequence. ( The number of time windows contained in the input sequence is input into the trained Hidden Markov Model (HMM), and the Viterbi algorithm can be used to solve for the optimal state sequence corresponding to the observation sequence. The Viterbi algorithm is based on the idea of ​​dynamic programming, which involves defining recursive variables. (in (For model parameters), representing the time step ( ). In state And before generation The maximum probability of each observation is calculated recursively to obtain the maximum probability state at each time step. Finally, the optimal state sequence is obtained by backtracking, which reflects the evolution of the channel state corresponding to the input feature sequence.

[0053] In S204, calculate the channel degradation prediction probability based on the optimal state sequence. and the state transition probability matrix of the model The carrier channel is in a slightly degraded state over the next few communication cycles. and severely deteriorated state The sum of these probabilities is the predicted degradation probability of the carrier channel. Specifically, the last state of the optimal state sequence is taken. As the current channel state, based on the state transition probability matrix Calculate the transition probability corresponding to the given row in the future. Within a communication cycle ( (For the preset number of prediction periods) transferred to and The cumulative probability. For example, if the current state is The communication cycle will be transferred to The probability is Transfer to The probability is The predicted probability of degradation in the next cycle is: If the current state is Then the degradation prediction probability is (in (The probability of maintaining a slightly degraded state), and so on. The predicted probability value ranges from 0 to 1, with a higher value indicating a higher probability of future channel degradation.

[0054] In some embodiments, step S3, prediction reliability assessment, addresses the problem of lack of controllable constraints on channel prediction results in the prior art. Even when channel prediction is introduced in the prior art, the reliability of the prediction results is not differentiated, making it difficult to determine whether the prediction conclusion is suitable for triggering communication mode switching, and easily leading to erroneous switching due to unreliable predictions. This embodiment quantifies the stability and reliability of the prediction results through multi-dimensional indicators, generating prediction reliability parameters. This effectively distinguishes between short-term, occasional interference and channel degradation with a continuous trend, providing reliability constraints for communication state transitions and ensuring the accuracy of switching decisions. The principle is that a single prediction result may be biased due to factors such as instantaneous interference and data fluctuations, while by analyzing indicators such as the historical consistency and fluctuation of the prediction results, the reliability of the prediction trend can be comprehensively judged.

[0055] In one embodiment, the prediction reliability assessment is based on three indicators: the magnitude of change in the degradation probability over multiple consecutive prediction periods, the consistency of the degradation probability under different statistical windows, and the number of times the degradation probability reverses its prediction within a short period of time. Each indicator reflects the reliability of the prediction results from different dimensions, and the specific definitions and calculation methods are as follows: Continuous prediction probability change index This indicator reflects the short-term stability of the forecast results and is calculated continuously. Within each prediction period, among which, The maximum fluctuation in the degradation prediction probability is calculated using the following formula: (The preset number of evaluation periods, for example, can be configured to 3-5.) in, For the first Channel degradation prediction probability for each prediction period For continuous The maximum predicted probability over a period of time. To be the minimum value, This is the average value. The smaller the value, the smaller the fluctuation of the continuous prediction results, the more stable the prediction trend, and the higher the credibility.

[0056] Forecast consistency index under different statistical windows This indicator assesses the robustness of predictions by comparing forecast results under different statistical window lengths. The basic statistical window length is selected. Based on length of , , Three predictions were made using the frame-level statistical feature time series data to obtain three prediction probabilities. , , The variance of these three predicted probabilities is calculated as a consistency index, and its calculation formula is as follows: in, It is the average of the three predicted probabilities. The smaller the value, the more consistent the prediction results are under different statistical windows, the stronger the robustness of the prediction model, and the higher its reliability.

[0057] Predicting the number of reversals indicator This indicator reflects the persistence of the predicted trend, based on recent statistics. Within a forecast period, The preset number of statistical periods, for example, can be configured to 4-6, reduces the number of reversals in the predicted probability change direction. Define the predicted probability change direction: if the... Predicted probability for each period Greater than the Predicted probability for each period If, then the direction of change is "upward"; if Less than If, then the direction of change is "downward"; if If the direction of change remains unchanged, then the change direction remains the same. When the directions of change are opposite for two consecutive cycles, it is determined as a prediction reversal. For example, the first... Cycle to number The cyclical change direction is "upward", the first Cycle to number If the direction of the cycle change is "downward", it is determined to be a reversal. The smaller the value, the more consistent the predicted trend is, the less frequent the fluctuations are, and the higher the reliability.

[0058] Based on the three evaluation indicators mentioned above, the final prediction confidence parameter is calculated using a weighted summation method. This parameter ranges from 0 to 1. A value closer to 1 indicates higher prediction confidence, while a value closer to 0 indicates lower prediction confidence. Please refer to [link / reference]. Figure 3 , Figure 3 This is a schematic diagram illustrating the calculation process for prediction confidence provided in an embodiment of this application. The specific calculation steps are as follows: In S301, the indicators are normalized. Since the three evaluation indicators have different dimensions and numerical ranges, each indicator is first normalized, mapping it to the [0,1] interval. For and The reverse normalization method is used, meaning that the larger the index value, the smaller the normalized result. The calculation formula is: in, The normalized index value, These are the original indicator values. This is the minimum value of the indicator. This is the maximum value of the indicator; for Similarly, reverse normalization is used to ensure that the fewer the number of prediction reversals, the larger the normalized result.

[0059] In S302, weight allocation. Weights are assigned to the normalized indicators based on the degree of influence of each evaluation indicator on the prediction reliability. , , The sum of weights satisfies For example, configurable (Weight of continuous change amplitude) (Window consistency weight) (Predicted reversal number weight), the weight allocation can be adjusted according to the actual communication scenario and prediction requirements.

[0060] In S303, a weighted summation is performed. The normalized index value is multiplied by its corresponding weight, and then summed to obtain the prediction confidence parameter. The calculation formula is as follows: in, , , These are the normalized continuous change magnitude index, the window consistency index, and the predicted reversal number index, respectively.

[0061] In some embodiments, step S4, the communication system state transition control, addresses the instability problem of dual-mode communication switching strategies in existing technologies. Existing technologies often use fixed thresholds or instantaneous channel indicators for switching, which can easily misjudge short-term interference as persistent degradation, leading to frequent switching and increased communication latency and power consumption. This embodiment, based on the channel degradation prediction probability and prediction reliability parameters obtained above, controls the orderly transition of the communication system between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state through preset judgment rules, achieving gradual switching and ensuring that communication mode adjustments match channel state evolution.

[0062] In one embodiment, the channel degradation prediction probability (hereinafter referred to as...) is combined with... ), prediction confidence parameters (hereinafter referred to as Given the current communication status, determine whether to trigger a state transition according to the following rules: Preset threshold definition: Set the first preset threshold. Second preset threshold Third preset threshold (satisfy This is used to divide the degradation probability into different intervals; a first confidence threshold is set. Second credibility threshold (satisfy These thresholds are used to classify different levels of prediction confidence. These thresholds can be flexibly configured according to the service requirements, equipment performance, and channel environment of the actual communication scenario, for example... It can be configured as the upper limit of the low degradation probability range. It can be configured to the upper limit of the medium degradation probability range. It can be configured as the lower limit of the high degradation probability range; It can be configured as the boundary between low and medium confidence levels. It can be configured as the boundary value between medium and high confidence levels.

[0063] Rules for transitioning from the initial state or other states to the carrier-enhanced communication state: When At any time, regardless The value determines whether the communication system maintains or migrates to carrier-enhanced communication; when and At the same time, the system will either remain in or migrate to carrier-enhanced communication state. This rule applies to scenarios where the channel condition is good or the predicted trend is unreliable.

[0064] The rule for transitioning from carrier-enhanced communication state to dual-link parallel communication state: When At any time, regardless The system transitions from carrier-enhanced communication to dual-link parallel communication depending on the value; when and The system also triggers this migration at the same time. This rule is applicable to scenarios where the channel degradation trend is clear or the prediction is highly reliable.

[0065] The rule for transitioning from a dual-link parallel communication state to a wireless master communication state: When and When the system transitions from a dual-link parallel communication state to a wireless primary communication state, this rule is applicable to scenarios where the channel is severely degraded and the prediction results are highly reliable.

[0066] State rollback rule: When the system is in a dual-link parallel communication state, if and This indicates that the channel state has returned to normal, and the system has reverted to carrier-enhanced communication state; when the system is in wireless primary communication state, if and This indicates that the channel degradation trend has eased, the system has reverted to a dual-link parallel communication state, and the carrier channel is gradually regaining its dominant position.

[0067] In one embodiment, the control execution for the carrier-enhanced communication state is as follows: In this state, the system maintains power line carrier communication as the primary communication link and improves anti-interference capability by adjusting communication parameters. The steps are as follows: increasing forward error correction (FEC) redundancy by increasing the number of redundant bits to enhance the error correction capability for transmitted erroneous data; adjusting carrier communication modulation parameters by selecting a modulation method with stronger anti-interference capability or reducing the modulation order to reduce the impact of channel noise on signal demodulation; and shortening the HPLC confirmation cycle to reduce the time the transmitter waits for ACK frames, accelerate the retransmission response speed of erroneous frames, and avoid error accumulation. This state is suitable for dealing with short-term or uncertain interference and can ensure communication stability without switching communication modes.

[0068] Control execution for dual-link parallel communication: In this state, while maintaining the primary carrier communication, the system activates the wireless communication link as a backup. The execution steps are as follows: Activate the wireless communication module, complete module initialization and parameter configuration; The wireless communication module and the wireless module of the peer device perform synchronization negotiation, including clock synchronization, frequency synchronization, and link parameter negotiation, to establish a stable wireless communication link; Monitor the transmission rate, bit error rate, and other quality indicators of the wireless link by sending test frames and feed them back to the communication control module; The carrier communication module continues to carry the main service data transmission, while the wireless link only transmits a small amount of test data or backup data to ensure service continuity. This state prepares for possible primary link switching, reducing switching latency and the risk of service interruption.

[0069] Control execution for the primary wireless communication state: In this state, the system switches the primary communication service to the wireless link. The execution steps are as follows: The communication control module uses a scheduling algorithm to smoothly switch the main service data originally carried by carrier communication to the wireless communication link, ensuring seamless data transmission; the power line carrier communication module is reserved as a backup link or low-speed communication channel to continue transmitting a small amount of non-critical data; the carrier communication module continuously collects channel operation data, calculates frame-level statistical characteristics, and feeds them back to the communication control module, providing a basis for subsequent state rollback. This state is suitable for scenarios with severely degraded carrier channels, ensuring communication continuity by switching the primary link.

[0070] In one embodiment, the system further includes updating system status information and historical data after completing the control execution of the current communication state: recording the current communication state (carrier enhancement, dual-link parallel, or wireless primary) to a status register, and simultaneously storing the degradation probability, prediction reliability parameters, and state transition decision results for the current prediction period, forming a historical record database to provide historical data support for subsequent prediction reliability assessment. After completing the status update, the system returns to the HPLC frame-level communication data acquisition stage, enters the next statistical period, and repeats the above frame-level feature calculation, channel degradation prediction, reliability assessment, and state transition control process, forming a complete closed-loop control mechanism to achieve dynamic anti-interference control under long-term operation.

[0071] This application presents a complete HPLC anti-interference solution through HPLC frame-level statistical feature calculation and time series construction, hidden Markov model-driven channel degradation probability prediction, multi-dimensional prediction reliability assessment, and two-parameter-based progressive state transition control. It can identify carrier channel degradation trends in advance, avoiding sudden communication interruptions; by constraining switching decisions with prediction reliability, it effectively reduces frequent switching caused by short-term interference, lowering communication jitter; progressive state transition ensures precise matching between communication mode adjustments and channel state evolution, improving system stability and reliability; furthermore, the method is implemented based on existing HPLC protocols and communication modules, resulting in low cost, strong adaptability, and applicability to existing HPLC / HRF dual-mode communication equipment through software upgrades, demonstrating significant practical value in complex interference scenarios such as low-voltage power distribution networks.

[0072] Please see Figure 4 , Figure 4 This is a schematic diagram of a system for HPLC anti-interference and dual-mode switching based on channel state prediction, provided in an embodiment of this application. Figure 4 As shown, system 400 includes: The statistical feature construction module 401 is used to calculate frame-level statistical features and construct a frame-level statistical feature time series based on HPLC frame-level communication data. The frame-level statistical features include at least frame error rate, average number of retransmissions, frame arrival time jitter and acknowledgment ratio. The degradation probability prediction module 402 is used to predict the degradation probability of the carrier channel based on the frame-level statistical feature time series. The prediction reliability assessment module 403 is used to assess the prediction reliability of the degradation prediction result based on the predicted degradation probability. The state transition control module 404 is used to control the communication system to perform state transitions between carrier-enhanced communication state, dual-link parallel communication state and wireless primary communication state based on the degradation probability and the prediction confidence.

[0073] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0074] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0075] Please see Figure 5 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 5 As shown, the electronic device 500 may include: The system includes at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502. The communication bus 502 is used to enable connection and communication between the components. The user interface 503 may include buttons, and optionally include a standard wired or wireless interface. The network interface 504 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0076] The processor 501 may include one or more processing cores and connect to various parts within the device 500 via various interfaces and lines. It implements the various functions and data processing of the device 500 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and by accessing data in the memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 501 may also integrate one or more combinations of CPU, GPU, and modem.

[0077] Memory 505 may include random access memory (RAM) or read-only memory (ROM). Optionally, memory 505 may include a non-transitory computer-readable medium for storing instructions, programs, code, code sets, or instruction sets. Figure 5 As shown, the memory 505, which serves as a computer storage medium, may contain an operating system, a network communication module, a user interface module, and program instructions.

[0078] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application 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 flowchart. When the computer program is executed by processor 501, it performs the functions defined in the methods of this application.

[0079] Another embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application described above.

[0080] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

Claims

1. A method for HPLC anti-interference and dual-mode switching based on channel state prediction, applied to a dual-mode communication system of power line carrier communication and wireless communication, characterized in that, include: Based on HPLC frame-level communication data, frame-level statistical features are calculated and a time series of frame-level statistical features is constructed. The frame-level statistical features include at least frame error rate, average number of retransmissions, frame arrival time jitter, and acknowledgment ratio. Based on the frame-level statistical feature time series, the degradation probability of the carrier channel is predicted; The reliability of the degradation prediction results is assessed based on the predicted degradation probability. Based on the degradation probability and the prediction confidence, the control communication system performs state transitions between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state.

2. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 1, characterized in that, The HPLC frame-level communication data includes at least: the total number of HPLC frames sent, the number of CRC check failure frames, the number of retransmitted frames, the number of ACK frames, the number of NACK frames, and the HPLC frame reception timestamp.

3. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 2, characterized in that, Calculating frame-level statistical features includes at least: The frame error rate is calculated based on the number of CRC check failure frames and the number of HPLC frames sent; the average number of retransmissions is calculated based on the number of retransmitted frames and the number of successful frames; the frame arrival time jitter is calculated based on the time interval between the timestamps of adjacent HPLC frames; and the acknowledgment ratio is calculated based on the number of ACK frames and the total number of acknowledgment-related frames.

4. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 1, characterized in that, Specifically, the predicted degradation probability of the carrier channel is obtained by normalizing the frame-level statistical feature time series and inputting it as the observation sequence into a pre-trained Hidden Markov Model to obtain the corresponding optimal channel state sequence, and calculating the predicted channel degradation probability of the carrier channel in subsequent communication cycles based on the state transition relationship of the Hidden Markov Model.

5. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 4, characterized in that, The hidden Markov model includes at least three channel states, corresponding to the normal state, slightly degraded state, and severely degraded state of the carrier channel, respectively, and the frame-level statistical features are used as observations to establish a probabilistic correlation with the channel states.

6. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 1, characterized in that, The reliability of the prediction is assessed based on at least one of the following metrics: The magnitude of the change in the degradation probability over multiple consecutive prediction periods, the consistency of the degradation probability under different statistical windows, and the number of times the degradation probability reverses its prediction in a short period of time.

7. The method for HPLC anti-interference and dual-mode switching based on channel state prediction according to claim 1, characterized in that, The control communication system performs state transitions between carrier-enhanced communication, dual-link parallel communication, and wireless primary communication states, specifically including: When the degradation probability is lower than the first preset threshold, or when the degradation probability is higher than the first preset threshold but the prediction confidence is lower than the first confidence threshold, the carrier enhancement communication state is maintained. When the degradation probability is higher than the second preset threshold, or when the degradation probability is higher than the first preset threshold and the prediction confidence is higher than the first confidence threshold, the system migrates from carrier-enhanced communication state to dual-link parallel communication state. When the degradation probability is higher than a third preset threshold and the prediction confidence is higher than a second confidence threshold, the system transitions from dual-link parallel communication state to wireless primary communication state.

8. A system for HPLC anti-interference and dual-mode switching based on channel state prediction, characterized in that, include: The statistical feature construction module is used to calculate frame-level statistical features and construct a frame-level statistical feature time series based on HPLC frame-level communication data. The frame-level statistical features include at least frame error rate, average retransmission count, frame arrival time jitter, and acknowledgment ratio. The degradation probability prediction module is used to predict the degradation probability of the carrier channel based on the frame-level statistical feature time series. The prediction reliability assessment module is used to assess the prediction reliability of the degradation prediction result based on the predicted degradation probability. The state transition control module is used to control the communication system to perform state transitions between carrier-enhanced communication state, dual-link parallel communication state, and wireless primary communication state based on the degradation probability and the prediction confidence.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The computer program instructions can be executed by a processor to implement the method as described in any one of claims 1 to 7.

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