A method and system for interference suppression in a multi-node power line carrier communication network

Abstract

This application provides a method and system for interference suppression in a multi-node power line carrier communication network. The method involves synchronously acquiring carrier signals from the connected power line to obtain the acquired signals from each communication node; extracting the original interference sensing vector from each communication node; determining the node-level interference feature vector and interference type label for each communication node based on the corresponding original interference sensing vector; uploading the corresponding node-level interference feature vector and interference type label to the network master node; performing spatial coupling modeling on the uploaded information to generate a network-level interference coupling model; and jointly optimizing the communication behavior of each communication node based on the network-level interference coupling model to generate a multi-node collaborative interference suppression strategy. The technical solution provided in this application can achieve refined perception and identification of interference from a global perspective, and based on this, make forward-looking collaborative decisions to improve the accuracy of communication network interference suppression.

Description

Technical Field

[0001] This application relates to the field of communication interference suppression technology, and more specifically, to a method and system for suppressing interference in multi-node power line carrier communication networks. Background Technology

[0002] Power line carrier communication (PLC) is a technology that uses existing power lines as the transmission medium for data communication. Due to its low deployment cost and the elimination of the need for additional wiring, it is widely used in smart meter reading, power distribution automation, and industrial field communication. However, because power lines are not designed for communication, their complex electrical environment and variable topology lead to severe interference problems in practical applications, especially in multi-node PLC networks.

[0003] In existing technologies, power line carrier communication networks typically employ node-independent interference detection and suppression methods. Each communication node judges and processes interference based on its received carrier signal. While this approach can mitigate local noise or simple interference to some extent, the differences in power line location, branch structure, and load conditions among different communication nodes mean that the interference states perceived by nodes often exhibit strong spatiotemporal correlations. Relying solely on single-node information makes it difficult to accurately characterize the propagation and superposition characteristics of interference throughout the network, easily leading to incomplete interference identification or inconsistent suppression strategies. Therefore, how to achieve refined interference perception and identification from a global perspective, and based on this, make forward-looking collaborative decisions to improve the accuracy of communication network interference suppression, remains a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for suppressing interference in multi-node power line carrier communication networks. It can achieve refined perception and identification of interference from a global perspective, and make forward-looking collaborative decisions based on this to improve the accuracy of interference suppression in communication networks.

[0005] In a first aspect, this application provides a method for suppressing interference in a multi-node power line carrier communication network, comprising the following steps:

[0006] In a power line carrier communication network, each communication node synchronously collects the carrier signal on the connected power line within a preset communication period, thereby obtaining the collected signal of each communication node.

[0007] The original interference sensing vector of each communication node is extracted from the corresponding acquired signal. Based on the original interference sensing vector, the node-level interference feature vector of each communication node is determined, and then the interference type label of each communication node is determined.

[0008] Each communication node uploads its corresponding node-level interference feature vector and interference type label to the network master node through the control channel. The network master node then uses the uploaded information to perform spatial coupling modeling to generate a network-level interference coupling model.

[0009] The network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy.

[0010] In some embodiments, the acquired signal includes a carrier communication baseband signal and a superimposed signal of power line background noise.

[0011] In some embodiments, extracting the original interference sensing vector of each communication node from the corresponding acquired signal specifically includes:

[0012] For each communication node, the acquired signals are preprocessed to obtain the corresponding time-domain signals;

[0013] The time-domain signal is divided into frames, and a short-time Fourier transform is performed on each frame to obtain the time-frequency distribution matrix of the communication node.

[0014] Interference sensing features are extracted based on the time-frequency distribution matrix to obtain the original interference sensing vector of the communication node, and then the original interference sensing vector of each communication node is obtained.

[0015] In some embodiments, determining the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector specifically includes:

[0016] For each communication node, the original interference sensing vector of the communication node is standardized.

[0017] The interference sensing features in the standardized original interference sensing vector are aggregated along the time and frequency dimensions to obtain multiple statistical features that can reflect the long-term stability and short-term burstiness of interference.

[0018] Determine the information contribution of each statistical feature;

[0019] Based on the corresponding information contribution, all statistical features are fused to obtain the node-level interference feature vector of the communication node, and then the node-level interference feature vector of each communication node is obtained.

[0020] In some embodiments, determining the interference type label of each communication node specifically includes:

[0021] For each communication node, the node-level interference feature vector of the communication node is subjected to feature validity check and scale consistency correction.

[0022] The corrected node-level interference feature vector is used as the basic input for interference type discrimination, and then matched with the preset interference discrimination rules to obtain multiple preliminary interference classification results.

[0023] Determine the interference membership degree between the communication node and each preliminary interference classification result;

[0024] The interference type with the highest membership degree in the preliminary classification result is taken as the dominant interference type of the communication node and labeled to generate the interference type label of the communication node, thereby obtaining the interference type label of each communication node.

[0025] In some embodiments, using the network master node to perform spatial coupling modeling on uploaded information to generate a network-level interference coupling model specifically includes:

[0026] The network master node uses the uploaded information as input to perform a horizontal comparative analysis of the interference characteristics of different communication nodes within the same time window, thereby identifying local interference in multiple communication nodes and common interference propagating across nodes.

[0027] The network master node determines the interference coupling strength between each communication node based on the local interference in multiple communication nodes, the common interference propagating across nodes, and the pre-established network topology.

[0028] Based on the interference coupling strength between various communication nodes, the propagation path and superposition mode of different interference types in the network are modeled, thereby generating a network-level interference coupling model.

[0029] In some embodiments, the network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node cooperative interference suppression strategy, specifically including:

[0030] The network master node performs a sensitivity analysis on the interference between communication nodes in the network-level interference coupling model, and then obtains the node interference sensitivity analysis results in the current power line network.

[0031] The node interference sensitivity analysis results are used to uniformly model the communication behavior of each communication node, thereby constructing a joint optimization space for multi-node communication behavior.

[0032] The network master node solves the joint optimization space according to the set optimization objective, and then generates differentiated communication control schemes for different communication nodes.

[0033] The differentiated communication control schemes are uniformly coordinated and their consistency is verified, thereby generating a multi-node collaborative interference suppression strategy.

[0034] Secondly, this application provides an interference suppression system for multi-node power line carrier communication networks, used to perform an interference suppression method for multi-node power line carrier communication networks, including:

[0035] The signal acquisition module is used in a power line carrier communication network to synchronously acquire the carrier signal on the connected power line within a preset communication period, thereby obtaining the acquired signal of each communication node.

[0036] The interference determination module is used to extract the original interference sensing vector of each communication node from the corresponding acquired signal, determine the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector, and then determine the interference type label of each communication node.

[0037] The model generation module is used by each communication node to upload the corresponding node-level interference feature vector and interference type label to the network master node through the control channel, and then use the network master node to perform spatial coupling modeling on the uploaded information to generate a network-level interference coupling model.

[0038] The strategy generation module is used by the network master node to jointly optimize the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy.

[0039] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described multi-node power line carrier communication network interference suppression method.

[0040] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for suppressing interference in multi-node power line carrier communication networks.

[0041] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0042] This application provides a method and system for interference suppression in a multi-node power line carrier communication network. In this system, each communication node synchronously acquires carrier signals from the connected power line within a preset communication period, thus obtaining the acquired signals of each communication node. The original interference sensing vector of each communication node is extracted from the acquired signals. Based on the original interference sensing vector, the node-level interference feature vector of each communication node is determined, thereby determining the interference type label of each communication node. Each communication node uploads its corresponding node-level interference feature vector and interference type label to the network master node via a control channel. The network master node then performs spatial coupling modeling on the uploaded information to generate a network-level interference coupling model. The network master node jointly optimizes the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node cooperative interference suppression strategy.

[0043] Therefore, this application firstly extracts the original interference sensing vector from each communication node and constructs a node-level interference feature vector and determines the interference type label based on it. This enables refined quantification and characterization of the interference state of each node, thereby obtaining multi-dimensional information such as the time domain, frequency domain, and statistical characteristics of the interference. This allows the network to accurately identify the type and intensity of interference at the node level. Secondly, each communication node uploads the node-level interference feature vector and interference type label to the network master node. The network master node then performs spatial coupling modeling to generate a network-level interference coupling model. This allows the local interference information scattered across each node to be aggregated into global interference cognition, achieving a refined characterization of the spatial distribution, propagation path, and coupling relationship of interference in the entire communication network. Finally, the network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model and generates a multi-node collaborative interference suppression strategy. This combines the local interference information of scattered nodes with the overall coupling relationship of the network, achieving refined perception and identification of interference from a global perspective. This improves the overall interference suppression accuracy, communication reliability, and resource utilization efficiency of the power line carrier communication network.

[0044] In summary, the technical solution adopted in this application can achieve refined perception and identification of interference from a global perspective, and make forward-looking collaborative decisions based on this to improve the accuracy of communication network interference suppression. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an exemplary flowchart of an interference suppression method for a multi-node power line carrier communication network according to some embodiments of this application;

[0047] Figure 2 This is an exemplary flowchart illustrating the extraction of the original interference sensing vectors of each communication node according to some embodiments of this application;

[0048] Figure 3 This is a schematic diagram of the structure of a multi-node power line carrier communication network interference suppression system according to some embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the structure of a computer device that implements a method for suppressing interference in a multi-node power line carrier communication network, according to some embodiments of this application. Detailed Implementation

[0050] 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 a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0051] This application provides a method and system for interference suppression in a multi-node power line carrier communication network. The core of the method involves each communication node synchronously acquiring carrier signals from the connected power line within a preset communication period, thus obtaining the acquired signals of each communication node. The original interference perception vector of each communication node is extracted from the corresponding acquired signals. Based on the original interference perception vector, the node-level interference feature vector of each communication node is determined, thereby determining the interference type label of each communication node. Each communication node uploads its corresponding node-level interference feature vector and interference type label to the network master node via a control channel. The network master node then performs spatial coupling modeling on the uploaded information to generate a network-level interference coupling model. The network master node jointly optimizes the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy. This approach allows for refined perception and identification of interference from a global perspective, and enables forward-looking collaborative decision-making to improve the accuracy of communication network interference suppression.

[0052] To better understand the above technical solutions, a detailed description of the technical solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. (Refer to...) Figure 1This figure is an exemplary flowchart of an interference suppression method for multi-node power line carrier communication networks according to some embodiments of this application. The figure mainly includes the following steps:

[0053] In step S101, in the power line carrier communication network, each communication node synchronously collects the carrier signal on the accessed power line within a preset communication period, thereby obtaining the collected signal of each communication node.

[0054] In its implementation, the power line carrier communication network first establishes a unified communication cycle and time reference under the control of the network master node or time coordination unit. Each communication node enters the same preset communication cycle under the constraint of this unified time reference, thus providing a time consistency basis for synchronous acquisition. Then, each communication node uses the unified communication cycle established in the previous step as input and simultaneously initiates its local acquisition process at the start of the communication cycle through a synchronous triggering mechanism, enabling each node to sample the power line signal it has accessed within the same time window. During synchronous sampling, each communication node first isolates and couples the high-voltage power signal on the power line with the carrier signal carrying the communication information through power line coupling and analog front-end processing circuits. Within a limited frequency band, the signal is filtered and amplitude-conditioned to obtain an analog carrier signal suitable for digital processing. This analog carrier signal serves as the input for the next step of analog-to-digital conversion. Subsequently, under the control of the synchronization trigger signal, each communication node performs an analog-to-digital conversion operation on the analog carrier signal, converting it into a discrete digital sampling signal. Since environmental noise, load disturbances, and external electromagnetic interference carried by the power line itself are simultaneously superimposed on the carrier signal, the resulting digital sampling signal naturally contains the carrier communication baseband signal and the superimposed power line background noise component. Finally, each communication node adds a corresponding time stamp and communication period stamp to the sampled digital signal, forming a collection signal that corresponds one-to-one with the preset communication period, thereby obtaining the collection signal of each communication node containing the superimposed signal of the carrier communication baseband signal and the power line background noise.

[0055] In step S102, the original interference sensing vector of each communication node is extracted from the corresponding acquired signal, and the node-level interference feature vector of each communication node is determined based on the corresponding original interference sensing vector, thereby determining the interference type label of each communication node.

[0056] Preferably, in some embodiments, reference is made to Figure 2 As shown in the figure, this is an exemplary flowchart of extracting the original interference sensing vectors of each communication node according to some embodiments of this application. In this embodiment, the extraction of the original interference sensing vectors of each communication node from the corresponding acquired signals can be achieved by the following steps:

[0057] In step S1021, for each communication node, the acquired signal of the communication node is preprocessed to obtain the corresponding time domain signal;

[0058] In step S1022, the time-domain signal is processed by framing, and a short-time Fourier transform is performed on each frame signal to obtain the time-frequency distribution matrix of the communication node.

[0059] In step S1023, interference sensing features are extracted based on the time-frequency distribution matrix to obtain the original interference sensing vector of the communication node, and then the original interference sensing vector of each communication node is obtained.

[0060] In the specific implementation process, firstly, for each communication node, the acquired signals can be preprocessed. That is, the communication node takes the acquired signals obtained synchronously in the previous stage as input and performs preprocessing operations on the acquired signals. By removing DC components, suppressing power frequency and its harmonic components, and limiting the analysis frequency band, the carrier communication baseband signal and its superimposed power line background noise are preserved in the target frequency band, thereby obtaining a time-domain signal suitable for subsequent analysis. Then, the time-domain signal can be framed. That is, taking the time-domain signal as the object, the time-domain signal is continuously framed according to the preset time window length and frame shift step size, so that adjacent frames have a certain overlap in time to ensure the continuous expression of interference changes. The resulting multi-frame time-domain signal is used as the direct input for short-time spectrum analysis. After completing the framing, the communication node performs short-time Fourier transform operations on each frame of time-domain signal, mapping the time-domain signal to the time-frequency domain, thereby constructing a time-frequency distribution matrix reflecting the energy change relationship between different time slices and different frequency components.

[0061] Furthermore, in the specific implementation process, interference sensing features can be extracted based on the time-frequency distribution matrix. Specifically, the communication node performs an integral operation along the frequency dimension of the matrix to calculate the instantaneous energy distribution of each frequency band within the corresponding time window, thus characterizing the strength of interference in different frequency bands. Then, using the instantaneous energy distribution of each frequency band obtained in the previous step as input, the spectral mutation rate is calculated by comparing the energy change rates of adjacent time windows or adjacent frequency bands. This reflects the rapid change characteristics of impulse interference or sudden interference in the frequency domain. Based on this, the signal power and estimated noise power are further separated using the instantaneous energy distribution and spectral mutation rate as criteria, and the instantaneous signal-to-noise ratio (SNR) change curve over time is calculated to characterize the dynamic impact of interference on communication quality. Finally, the instantaneous energy distribution, spectral mutation rate, and instantaneous SNR change curve can be used as interference sensing features, and the feature vector composed of all interference sensing features can be used as the original interference sensing vector of the corresponding communication node. The original interference sensing vector of each communication node can then be obtained in the above manner.

[0062] In some embodiments, determining the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector can be achieved in the following manner:

[0063] For each communication node, the original interference sensing vector of the communication node is standardized.

[0064] The interference sensing features in the standardized original interference sensing vector are aggregated along the time and frequency dimensions to obtain multiple statistical features that can reflect the long-term stability and short-term burstiness of interference.

[0065] Determine the information contribution of each statistical feature;

[0066] Based on the corresponding information contribution, all statistical features are fused to obtain the node-level interference feature vector of the communication node, and then the node-level interference feature vector of each communication node is obtained.

[0067] In the specific implementation process, firstly, for each communication node, the original interference sensing vector of the communication node can be standardized. That is, for various interference sensing features in the original interference sensing vector, the scale and amplitude of features with different dimensions and value ranges are unified, so that the interference sensing features are comparable within the same numerical range, thus obtaining the standardized original interference sensing vector. Then, taking the standardized original interference sensing vector as the analysis object, the interference sensing features are aggregated along the time dimension and the frequency dimension, respectively. By calculating the average level, fluctuation amplitude and trend of the interference sensing features in the time dimension, and calculating the energy concentration and distribution dispersion in the frequency dimension, multiple statistical features that can reflect the long-term stability and short-term burstiness of interference can be extracted. Secondly, the information contribution of each statistical feature can be determined. Using multiple consecutive communication cycles as the time scale, a corresponding feature sample sequence can be constructed for each statistical feature. The variance of the statistical feature in this feature sample sequence can be calculated, and the result can be used as the sensitivity to change. This sensitivity to change indicates how sensitive the statistical feature is to changes in interference conditions; the larger the change, the more significant the response of the statistical feature to changes in interference, and the higher its information contribution potential. A communication quality index sequence is introduced as a reference. This communication quality index sequence includes indicators such as changes in bit error rate or effective signal-to-noise ratio. The correlation coefficient between the feature sample sequence and this communication quality index sequence can be calculated as the interference correlation degree, which is used to measure the statistical feature. The higher the interference correlation, the better the statistical feature reflects the actual interference impact in terms of its explanatory power for communication performance degradation. The average correlation between a statistical feature and other statistical features can be calculated to represent the amount of independent information provided by that feature; the lower the average correlation, the more independent information the feature contains. After obtaining the sensitivity to change, interference correlation, and average correlation of the statistical feature, the communication node uses these three indicators as input, normalizes them, and performs weighted fusion according to preset weights. The final result is then used as the information contribution of the statistical feature, which characterizes its contribution to interference suppression information. The information contribution of each statistical feature can be obtained through this method.

[0068] In addition, in the specific implementation process, all statistical features can be fused according to their corresponding information contribution. That is, the communication node performs weighted fusion processing on all statistical features according to their corresponding information contribution, and the processing result is used as the node-level interference feature vector of the communication node. Through fusion, the statistical features with higher information contribution can occupy a greater weight in the fusion result, thereby constructing a node-level interference feature vector that can comprehensively and stably represent the interference state of the communication node. The node-level interference feature vector of each communication node can be obtained in the above way.

[0069] In some embodiments, the interference type label of each communication node can be determined in the following manner:

[0070] For each communication node, the node-level interference feature vector of the communication node is subjected to feature validity check and scale consistency correction.

[0071] The corrected node-level interference feature vector is used as the basic input for interference type discrimination, and then matched with the preset interference discrimination rules to obtain multiple preliminary interference classification results.

[0072] Determine the interference membership degree between the communication node and each preliminary interference classification result;

[0073] The interference type with the highest membership degree in the preliminary classification result is taken as the dominant interference type of the communication node and labeled to generate the interference type label of the communication node, thereby obtaining the interference type label of each communication node.

[0074] In the specific implementation process, firstly, for each communication node, the node-level interference feature vector is used as input, and the validity of each dimension of the feature is checked. By analyzing the stability of the feature within the current communication cycle, its noise sensitivity, and whether there are any abnormal mutations, feature components that contribute little or are unreliable to interference discrimination are eliminated or suppressed. At the same time, scale consistency correction is performed on the retained features to make features with different physical meanings and dimensions comparable within a unified numerical range, thus obtaining the corrected node-level interference feature vector. Then, the corrected node-level interference feature vector is used as the basic input for interference type discrimination and is matched with a pre-built interference discrimination rule base. The interference discrimination rules are set based on the differences in time-domain fluctuation characteristics, frequency-domain occupancy characteristics, and statistical stability of different interferences. By comparing each rule, multiple possible preliminary interference classification results are generated. Furthermore, the interference membership degree between the communication node and each preliminary interference classification result can be determined. This interference membership degree is used to quantify the matching degree between the current node-level interference feature vector and various interference feature patterns. Specifically, it calculates the similarity between the corrected node-level interference feature vector frequency and each interference type template in each interference discrimination rule base. The calculation result can then be used as the interference membership degree between the communication node and the corresponding preliminary interference classification result. Finally, the interference membership degrees corresponding to all preliminary interference classification results are compared. The preliminary interference classification result with the highest membership degree is selected, and the corresponding interference type is determined as the dominant interference type of the communication node. This is then labeled to generate the interference type label for the communication node, thus completing the determination of the interference type label for each communication node.

[0075] It should be noted that by extracting the original interference sensing vector from each communication node and constructing node-level interference feature vectors and determining interference type labels based on them, it is possible to achieve fine-grained quantification and characterization of the interference state of each node, thereby obtaining multi-dimensional information such as the time domain, frequency domain, and statistical characteristics of the interference. This not only enables the network to accurately identify the type and intensity of interference at the node level, but also provides high-quality input for subsequent multi-node information aggregation and network-level analysis, thereby constructing the spatial distribution and coupling relationship of interference from a global perspective.

[0076] In step S103, each communication node uploads its corresponding node-level interference feature vector and interference type label to the network master node through the control channel, and then uses the network master node to perform spatial coupling modeling on the uploaded information to generate a network-level interference coupling model.

[0077] In the specific implementation process, each communication node first uploads the node-level interference feature vector and interference type label as the object, and attaches the node identifier and the corresponding communication period time identifier. The data is then encapsulated and sent through the control channel, thereby ensuring that the network master node can distinguish the interference information of different nodes and their corresponding time slices.

[0078] In some embodiments, using the network master node to perform spatial coupling modeling on the uploaded information to generate a network-level interference coupling model can be specifically done in the following way:

[0079] The network master node uses the uploaded information as input to perform a horizontal comparative analysis of the interference characteristics of different communication nodes within the same time window, thereby identifying local interference in multiple communication nodes and common interference propagating across nodes.

[0080] The network master node determines the interference coupling strength between each communication node based on the local interference in multiple communication nodes, the common interference propagating across nodes, and the pre-established network topology.

[0081] Based on the interference coupling strength between various communication nodes, the propagation path and superposition mode of different interference types in the network are modeled, thereby generating a network-level interference coupling model.

[0082] In the specific implementation process, firstly, the network master node takes the node-level interference feature vectors and interference type labels (i.e., uploaded information) uploaded by each communication node through the control channel as input, and performs a horizontal comparative analysis of the interference characteristics of different communication nodes within the same time window. By comparing the similarity, abrupt change time points, and spectral characteristics of each node-level interference feature vector, it identifies interference events that occur synchronously or have highly consistent trends in multiple communication nodes, thereby distinguishing between local interference (occurring only in a few nodes) and common interference propagating across nodes (occurring simultaneously or successively in multiple nodes). Then, based on the local interference and common interference identified in the previous step, and combined with the pre-established power line network topology, physical connection methods between nodes, and node location distribution, the network master node quantifies the degree of interference impact between each communication node, determining the interference coupling strength between nodes. This interference coupling strength is used to characterize the possibility and magnitude of interference propagation between different nodes. In actual implementation, this interference coupling strength can be determined by the following formula:

[0083]

[0084] in, This represents the interference coupling strength between communication node i and communication node k. This represents the similarity between the node-level interference feature vector of communication node i and the node-level interference feature vector of communication node k. This represents the shortest path length between communication node i and communication node k in the network topology. Finally, the propagation paths and superposition methods of different interference types in the network can be modeled based on the interference coupling strength between each communication node. That is, the network master node takes the above interference coupling strength and the interference type of each communication node as input to systematically model the possible propagation paths, superposition methods and impact ranges of different interference types in the network. Taking into account the spatial correlation, time delay characteristics and spectral overlap characteristics of interference, a network-level interference coupling model can be formed that can comprehensively describe the spatial distribution, coupling relationship and propagation law of interference in multi-node power line carrier communication networks.

[0085] It should be noted that by uploading node-level interference feature vectors and interference type labels to the network master node through each communication node, and using the network master node to perform spatial coupling modeling to generate a network-level interference coupling model, the local interference information scattered across each node can be aggregated into a global interference cognition. This enables a refined characterization of the spatial distribution, propagation path, and coupling relationship of interference in the entire communication network. It can not only identify local interference and common interference propagating across nodes from a global perspective, but also quantify the intensity of interference interactions between nodes, providing reliable global input for the network master node, thereby supporting forward-looking collaborative decision-making and resource scheduling optimization.

[0086] In step S104, the network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy.

[0087] In some embodiments, the network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node cooperative interference suppression strategy. Specifically, this can be achieved in the following manner:

[0088] The network master node performs a sensitivity analysis on the interference between communication nodes in the network-level interference coupling model, and then obtains the node interference sensitivity analysis results in the current power line network.

[0089] The node interference sensitivity analysis results are used to uniformly model the communication behavior of each communication node, thereby constructing a joint optimization space for multi-node communication behavior.

[0090] The network master node solves the joint optimization space according to the set optimization objective, and then generates differentiated communication control schemes for different communication nodes.

[0091] The differentiated communication control schemes are uniformly coordinated and their consistency is verified, thereby generating a multi-node collaborative interference suppression strategy.

[0092] In the specific implementation process, firstly, the network master node uses a network-level interference coupling model as input to perform a sensitivity analysis of interference between communication nodes. By quantifying the response degree of each node to interference propagation from other nodes, the interference superposition effect, and the impact on communication performance, the interference sensitivity analysis results of each node in the current power line network are obtained. These results reflect the vulnerability and potential risks of different nodes in the interference environment, providing a basis for subsequent communication behavior modeling. Then, the network master node uses the node interference sensitivity analysis results as constraints to uniformly model the communication behavior of each communication node. Communication parameters such as node transmit power, carrier scheduling, transmission time window, and channel usage strategy are included in the optimization variables to construct a joint optimization space for multi-node communication behavior, comprehensively considering the interference interaction between nodes and the overall network communication requirements. Furthermore, based on preset optimization objectives, such as maximizing communication reliability, minimizing bit error rate, or suppressing interference superposition effects, the network master node solves the joint optimization space. Through multi-objective optimization algorithms or iterative scheduling strategies, differentiated communication control schemes are generated for different communication nodes, enabling each node to adaptively adjust while considering its own communication needs and the overall network interference state. Finally, the network master node coordinates and verifies the consistency of the generated differentiated communication control schemes, checks whether there are conflicts in the scheduling of each node and whether the communication resources are reasonably allocated, and makes necessary adjustments and synchronizations. Ultimately, a multi-node collaborative interference suppression strategy covering the entire network is formed, enabling the entire power line carrier communication network to achieve collaborative optimization and efficient interference suppression in complex interference environments.

[0093] It should be noted that by using the network master node to jointly optimize the communication behavior of each communication node based on the network-level interference coupling model and generate a multi-node collaborative interference suppression strategy, the local interference information of scattered nodes can be combined with the overall network coupling relationship. This allows for refined perception and identification of interference from a global perspective, quantifying the sensitivity and influence of each node in the interference propagation chain. Based on this global understanding, the network master node can predict the possible evolution path of interference in advance and coordinate the communication parameters and scheduling strategies of each node to achieve forward-looking collaborative decision-making. This enables the communication behavior of different nodes to complement and optimize each other in the interference environment, thereby improving the overall interference suppression accuracy, communication reliability, and resource utilization efficiency of the power line carrier communication network.

[0094] Therefore, this application firstly extracts the original interference sensing vector from each communication node and constructs a node-level interference feature vector and determines the interference type label based on it. This enables refined quantification and characterization of the interference state of each node, thereby obtaining multi-dimensional information such as the time domain, frequency domain, and statistical characteristics of the interference. This allows the network to accurately identify the type and intensity of interference at the node level. Secondly, each communication node uploads the node-level interference feature vector and interference type label to the network master node. The network master node then performs spatial coupling modeling to generate a network-level interference coupling model. This allows the local interference information scattered across each node to be aggregated into global interference cognition, achieving a refined characterization of the spatial distribution, propagation path, and coupling relationship of interference in the entire communication network. Finally, the network master node performs joint optimization of the communication behavior of each communication node based on the network-level interference coupling model and generates a multi-node collaborative interference suppression strategy. This combines the local interference information of scattered nodes with the overall coupling relationship of the network, achieving refined perception and identification of interference from a global perspective. This improves the overall interference suppression accuracy, communication reliability, and resource utilization efficiency of the power line carrier communication network.

[0095] In summary, the technical solution adopted in this application can achieve refined perception and identification of interference from a global perspective, and make forward-looking collaborative decisions based on this to improve the accuracy of communication network interference suppression.

[0096] Furthermore, in another aspect of this application, in some embodiments, this application provides an interference suppression system for multi-node power line carrier communication networks, referencing... Figure 3 The figure is a schematic diagram of the structure of a multi-node power line carrier communication network interference suppression system according to some embodiments of this application. The multi-node power line carrier communication network interference suppression system includes:

[0097] The signal acquisition module 201 is used in a power line carrier communication network to synchronously acquire the carrier signal on the accessed power line within a preset communication period, thereby obtaining the acquired signal of each communication node.

[0098] The interference determination module 202 is used to extract the original interference sensing vector of each communication node from the corresponding acquired signal, determine the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector, and then determine the interference type label of each communication node.

[0099] The model generation module 203 is used to upload the corresponding node-level interference feature vector and interference type label to the network master node through the control channel, and then use the network master node to perform spatial coupling modeling on the uploaded information to generate a network-level interference coupling model.

[0100] The strategy generation module 204 is used by the network master node to jointly optimize the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy.

[0101] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described multi-node power line carrier communication network interference suppression method.

[0102] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device implementing a multi-node power line carrier communication network interference suppression method according to some embodiments of this application. The multi-node power line carrier communication network interference suppression method in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0103] Processor 301 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC). An integrated circuit (ASIC) or one or more are used to control the execution of the interference suppression method for multi-node power line carrier communication networks in this application.

[0104] The communication bus 302 can be used to transmit information between the aforementioned components.

[0105] Memory 303 can be a read-only memory (ROM). ROM (Read-Only Memory) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable read-only memory. EEPROM (Electronic EPROM) and Compact Disc (CD-ROM) Memory The memory 303 may be an optical disc, a disk, or other optical disk storage device (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), a magnetic disk, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 303 may be independent and connected to the processor 301 via a communication bus 302. Alternatively, the memory 303 may be integrated with the processor 301.

[0106] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the interference suppression method for multi-node power line carrier communication networks can be achieved by the processor 301 and one or more software modules in the program code in the memory 303.

[0107] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0108] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core processor. A processor, or a multi-core processor. Processor. Here, processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0109] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0110] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for suppressing interference in multi-node power line carrier communication networks.

[0111] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0112] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for suppressing interference in a multi-node power line carrier communication network, characterized in that, Includes the following steps: In a power line carrier communication network, each communication node synchronously collects the carrier signal on the connected power line within a preset communication period, thereby obtaining the collected signal of each communication node. The original interference sensing vectors of each communication node are extracted from the corresponding acquired signals. Based on the original interference sensing vectors, the node-level interference feature vectors of each communication node are determined. For each communication node, the node-level interference feature vectors of the communication node are subjected to feature validity checks and scale consistency correction. The corrected node-level interference feature vectors are used as the basic input for interference type discrimination and then matched with the preset interference discrimination rules to obtain multiple preliminary interference classification results. The interference membership degree between the communication node and each preliminary interference classification result is determined. The interference type in the preliminary interference classification result with the largest interference membership degree is taken as the dominant interference type of the communication node and labeled to generate the interference type label of the communication node, thereby obtaining the interference type label of each communication node. Each communication node uploads its corresponding node-level interference feature vector and interference type label to the network master node via a control channel. The network master node uses the uploaded information as input to perform a horizontal comparison analysis of the interference features of different communication nodes within the same time window, thereby identifying local interference and cross-node propagating common interference in multiple communication nodes. The network master node determines the interference coupling strength between each communication node based on the local interference and cross-node propagating common interference in multiple communication nodes and the pre-established network topology relationship. Based on the interference coupling strength between each communication node, the propagation path and superposition mode of different interference types in the network are modeled, thereby generating a network-level interference coupling model. The network master node performs sensitivity analysis on the interference between communication nodes in the network-level interference coupling model, thereby obtaining the node interference sensitivity analysis results in the current power line network. The node interference sensitivity analysis results are used to uniformly model the communication behavior of each communication node, thereby constructing a joint optimization space for multi-node communication behavior. The network master node solves the joint optimization space according to the set optimization objective, thereby generating differentiated communication control schemes for different communication nodes. The differentiated communication control schemes are uniformly coordinated and verified for consistency, thereby generating a multi-node cooperative interference suppression strategy.

2. The interference suppression method for a multi-node power line carrier communication network as described in claim 1, characterized in that, The acquired signals include carrier communication baseband signals and power line background noise superimposed signals.

3. The interference suppression method for a multi-node power line carrier communication network as described in claim 1, characterized in that, Extracting the original interference sensing vector of each communication node from the corresponding acquired signals specifically includes: For each communication node, the acquired signals are preprocessed to obtain the corresponding time-domain signals; The time-domain signal is divided into frames, and a short-time Fourier transform is performed on each frame to obtain the time-frequency distribution matrix of the communication node. Interference sensing features are extracted based on the time-frequency distribution matrix to obtain the original interference sensing vector of the communication node, and then the original interference sensing vector of each communication node is obtained.

4. The interference suppression method for a multi-node power line carrier communication network as described in claim 1, characterized in that, Determining the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector specifically includes: For each communication node, the original interference sensing vector of the communication node is standardized. The interference sensing features in the standardized original interference sensing vector are aggregated along the time and frequency dimensions to obtain multiple statistical features that can reflect the long-term stability and short-term burstiness of interference. Determine the information contribution of each statistical feature; Based on the corresponding information contribution, all statistical features are fused to obtain the node-level interference feature vector of the communication node, and then the node-level interference feature vector of each communication node is obtained.

5. A multi-node power line carrier communication network interference suppression system, used to execute the multi-node power line carrier communication network interference suppression method as described in any one of claims 1 to 4, characterized in that, include: The signal acquisition module is used in a power line carrier communication network to synchronously acquire the carrier signal on the connected power line within a preset communication period, thereby obtaining the acquired signal of each communication node. The interference determination module is used to extract the original interference sensing vector of each communication node from the corresponding acquired signal, determine the node-level interference feature vector of each communication node based on the corresponding original interference sensing vector, and then determine the interference type label of each communication node. The model generation module is used by each communication node to upload the corresponding node-level interference feature vector and interference type label to the network master node through the control channel, and then use the network master node to perform spatial coupling modeling on the uploaded information to generate a network-level interference coupling model. The strategy generation module is used by the network master node to jointly optimize the communication behavior of each communication node based on the network-level interference coupling model, thereby generating a multi-node collaborative interference suppression strategy.

6. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the interference suppression method for multi-node power line carrier communication networks as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the interference suppression method for multi-node power line carrier communication networks as described in any one of claims 1 to 4.

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