Dynamic routing self-adaptive low-voltage power grid carrier communication networking method and system

Abstract

The invention discloses a dynamic routing self-adaptive low-voltage power grid carrier communication networking method and system, and relates to the technical field of carrier communication, and the method comprises the steps: obtaining power grid load data and communication quality data, carrying out the feature extraction of the power grid load data, and obtaining power grid load features; constructing a channel quality model based on the power grid load characteristics and performing prediction to obtain a channel quality prediction value; performing node selection on communication nodes based on the channel quality predicted value and the communication quality data to obtain backup routing nodes; and formulating a route switching strategy according to the backup route node, and realizing low-voltage power grid carrier communication networking based on dynamic route self-adaption. According to the method, the problems of fixed routing strategy and insufficient channel quality prediction capability in traditional low-voltage power grid carrier communication are solved, and dynamic optimization and adaptive adjustment of a communication network are realized through combination of multi-dimensional data analysis and an intelligent decision mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of carrier communication, and particularly to a method and system for low-voltage power grid carrier communication networking with dynamic routing adaptability. Background Art

[0002] With the deepening of the construction of smart power grids, the importance of power communication networks has become increasingly prominent; as an important part of smart power grids, low-voltage power grid carrier communication technology has been widely used in the fields of smart meter data collection, distribution automation, and power consumption information collection due to its advantages of using existing power lines for communication, no need for additional wiring, and low deployment cost; however, as a communication channel, the low-voltage power grid has obvious time-varying characteristics, and factors such as power grid load fluctuations and harmonic interference will cause significant changes in channel characteristics; traditional low-voltage power grid carrier communication networking methods mainly adopt static routing strategies, which can ensure basic communication requirements when the network topology is relatively stable, but it is difficult to adapt to the complex and changeable power grid operation environment; when the power grid load fluctuates violently or the harmonic interference increases, the communication link quality will significantly decline, resulting in an increase in communication delay and packet loss rate, seriously affecting the reliability and real-time performance of the communication network.

[0003] At present, the industry has carried out a large number of studies on the problem of low-voltage power grid carrier communication networking and proposed routing selection methods based on channel quality indicators and network topology characteristics; these methods have improved the adaptability of communication networks to a certain extent, but there are still the following deficiencies: First, existing methods often only consider real-time communication quality indicators and lack the ability to predict future changes in channel quality, and cannot make routing adjustments in advance; second, the relationship between power grid load characteristics and communication quality is not accurately modeled, and it is difficult to accurately reflect the impact of load volatility and harmonic content factors on communication performance; third, the routing switching strategy is relatively simple and does not fully consider the load correlation between nodes, which is likely to cause local network congestion; these problems seriously restrict the performance improvement of low-voltage power grid carrier communication networks. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, and such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method for low-voltage power grid carrier communication networking with dynamic routing adaptability, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for forming a low-voltage power grid carrier communication network with dynamic routing adaptability, which includes obtaining power grid load data and communication quality data, extracting features from the power grid load data to obtain power grid load characteristics; constructing a channel quality model based on the power grid load characteristics and making a prediction to obtain a channel quality prediction value; selecting communication nodes based on the channel quality prediction value and the communication quality data to obtain backup routing nodes; and formulating a routing switching strategy according to the backup routing nodes to realize the formation of a low-voltage power grid carrier communication network with dynamic routing adaptability.

[0008] As a preferred solution of the method for forming a low-voltage power grid carrier communication network with dynamic routing adaptability according to the present invention, wherein: the power grid load data includes voltage data and current data; the communication quality data includes communication delay and packet loss rate; the power grid load characteristics include load volatility and harmonic content data.

[0009] As a preferred solution of the method for forming a low-voltage power grid carrier communication network with dynamic routing adaptability according to the present invention, wherein: the channel quality prediction value includes predicted communication delay and predicted packet loss rate; constructing a channel quality model based on the power grid load characteristics and making a prediction to obtain a channel quality prediction value means analyzing the power grid load characteristics, calculating the influence of the power grid load characteristics on communication delay and packet loss rate respectively, constructing a channel quality model, and then obtaining the predicted communication delay and predicted packet loss rate.

[0010] As a preferred solution of the method for forming a low-voltage power grid carrier communication network with dynamic routing adaptability according to the present invention, wherein: obtaining the predicted communication delay includes the following steps: representing the influence of load volatility on communication delay by using a linear function, and the specific formula is as follows: ; wherein, is the influence of load volatility on communication delay; is the influence coefficient of load volatility on communication delay; is the load volatility at the th moment; introducing the influence of harmonic content on communication delay and using an exponential term to limit the unlimited increase of high harmonic content on communication delay, and the specific formula is as follows: ; wherein, is the influence of harmonic content on communication delay; is the influence coefficient of harmonic content on communication delay; is the Harmonic content data at a moment; based on the reference value of communication delay, combining the influence of load volatility and harmonic content on delay to obtain the predicted communication delay; the specific formula for the predicted communication delay is as follows: ; Wherein, is the predicted communication delay at the moment; is the reference value of communication delay; is the influence coefficient of load volatility on communication delay; is at the moment of load volatility; is the influence coefficient of harmonic content on communication delay; is at the moment of harmonic content data.

[0011] As a preferred scheme of the low-voltage power grid carrier communication networking method for dynamic routing adaptation described in the present invention, wherein: obtaining the predicted packet loss rate includes the following steps: using a fractional function to simulate the influence of load volatility and harmonic content on the packet loss rate, and the specific formula is as follows: ; ; Wherein, is the influence of load volatility on the packet loss rate; is the influence coefficient of load volatility on the packet loss rate; is at the moment of load volatility; is the influence of harmonic content on the packet loss rate; is the influence coefficient of harmonic content on the packet loss rate; is at the moment of harmonic content data; based on the reference value of the packet loss rate, combining the influence of load volatility and harmonic content on the packet loss rate to obtain the predicted packet loss rate; the specific formula for the predicted packet loss rate is as follows: ; Wherein, is the predicted packet loss rate at the moment; is the reference value of the packet loss rate; is the influence coefficient of load volatility on the packet loss rate; is at the moment of load volatility; is the influence coefficient of harmonic content on the packet loss rate; is at the moment of harmonic content data.

[0012] As a preferred solution of the low-voltage power grid carrier communication networking method with dynamic routing adaptation according to the present invention, wherein: the node selection of communication nodes based on the channel quality prediction value and the communication quality data includes the following steps: judging based on the channel quality prediction value and the communication quality data, if the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the first cost score; if the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the second cost score; if the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the third cost score; if the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the fourth cost score; the node selection of communication nodes based on the channel quality prediction value and the communication quality data further includes the following steps: obtain all adjacent nodes of the current communication node, obtain the routing switching cost values of all adjacent nodes, compare according to the routing switching cost values of the adjacent nodes and the routing switching threshold, if the routing switching cost value of the adjacent node is less than the routing switching threshold, then classify the corresponding adjacent node as a backup routing node; if the routing switching cost value of the adjacent node is greater than or equal to the routing switching threshold, then determine that the corresponding adjacent node is not a backup routing node.

[0013] As a preferred solution of the low-voltage power grid carrier communication networking method with dynamic routing adaptation according to the present invention, wherein: formulating a routing switching strategy according to the backup routing nodes includes the following steps: obtain the load volatility rate and harmonic content data of the backup routing nodes based on all the backup routing nodes of the current communication node, calculate the load correlation coefficient between the current communication node and the backup routing nodes according to the load volatility rate and harmonic content data of the backup routing nodes; compare the load correlation coefficients of all the backup routing nodes, and take the backup routing node with the lowest load correlation coefficient as the target routing node; establish a communication link between the current communication node and the target routing node, and disconnect the original communication link.

[0014] In a second aspect, to further solve the security problems existing in carrier communication, an embodiment of the present invention provides a low-voltage power grid carrier communication networking system with dynamic routing adaptability, which includes: a data acquisition module for acquiring power grid load data and communication quality data, extracting features from the power grid load data to obtain power grid load characteristics; a model construction module for constructing a channel quality model based on the power grid load characteristics and performing prediction to obtain a channel quality prediction value; a node selection module for selecting communication nodes based on the channel quality prediction value and the communication quality data to obtain backup routing nodes; and a routing switching module for formulating a routing switching strategy according to the backup routing nodes to implement low-voltage power grid carrier communication networking based on dynamic routing adaptability.

[0015] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the method for low-voltage power grid carrier communication networking with dynamic routing adaptability as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the method for low-voltage power grid carrier communication networking with dynamic routing adaptability as described in the first aspect of the present invention is implemented.

[0017] Advantages of the present invention: By simultaneously collecting power grid load data such as voltage and current and communication quality data such as communication delay and packet loss rate, and extracting key features such as load volatility and harmonic content, the present invention establishes an association basis between the power grid operation state and communication performance for the system; by constructing a channel quality model, the influence mechanism of load characteristics on communication performance is accurately described, and accurate prediction of the change trend of channel quality is realized; based on the comparative analysis of the predicted value and the real-time value, a four-level routing switching cost evaluation mechanism is established, and backup nodes are screened in combination with the routing switching threshold, effectively avoiding the instability caused by frequent switching while ensuring the flexibility of the communication network; by introducing load correlation analysis and selecting the optimal target routing node for link switching, the load balancing effect is significantly improved; not only the problems of fixed routing strategy and insufficient channel quality prediction ability in traditional low-voltage power grid carrier communication are solved, but also through the combination of multi-dimensional data analysis and intelligent decision-making mechanism, the dynamic optimization and adaptive adjustment of the communication network are realized, which can effectively improve the reliability, real-time performance and adaptability of the communication network, and provide a reliable communication guarantee for the data acquisition and distribution automation services of smart grids. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them: Figure 1 It is the overall flowchart of the dynamic routing adaptive low-voltage power grid carrier communication networking method in Embodiment 1.

[0019] Figure 2 It is the flowchart for constructing the channel quality model in Embodiment 1.

[0020] Figure 3 It is the node selection flowchart in Embodiment 1.

[0021] Figure 4 It is the structural schematic diagram of the computer device in Embodiment 3. Detailed implementation manners

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the drawings in the specification.

[0023] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0025] Embodiment 1 Refer to Figures 1 to 3 , which is the first embodiment of the present invention. This embodiment provides a dynamic routing adaptive low-voltage power grid carrier communication networking method.

[0026] The existing low-voltage power grid carrier communication networking methods mainly have the following problems: First, the existing methods often only consider real-time communication quality indicators, lack the ability to predict future channel quality changes, and cannot make routing adjustments in advance; second, the relationship modeling between the power grid load characteristics and communication quality is not precise enough, making it difficult to accurately reflect the influence of load volatility and harmonic content factors on communication performance; third, the routing switching strategy is relatively simple, and the load correlation between nodes is not fully considered, easily leading to local network congestion; these problems seriously restrict the performance improvement of the low-voltage power grid carrier communication network.

[0027] This application provides a solution that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail on how to implement the dynamic routing adaptive low-voltage power grid carrier communication networking method.

[0028] Figure 1 The overall flowchart of the dynamic routing adaptive low-voltage power grid carrier communication networking method is shown, including: S1: Obtain power grid load data and communication quality data, extract features from the power grid load data, and obtain power grid load characteristics.

[0029] In the embodiments of this application, the power grid load data includes voltage data and current data.

[0030] Furthermore, obtaining power grid load data means setting voltage sensors and current sensors in the power grid, using the voltage sensors to obtain voltage data, and using the current sensors to obtain current data.

[0031] In the embodiments of this application, the communication quality data includes communication delay and packet loss rate.

[0032] Furthermore, obtaining communication quality data means using a network detection device to obtain communication delay and packet loss rate.

[0033] In the embodiments of this application, the power grid load characteristics include load volatility and harmonic content data.

[0034] Furthermore, extracting features from the power grid load data to obtain power grid load characteristics includes the following steps: Calculate the load volatility according to the voltage data and current data, where the load is the product of the voltage data and the current data, and the specific formula for the load volatility is as follows: ; Where, is the load volatility at the th moment; is the voltage data at the th moment; is the current data at the th moment; is the time interval; is the rated voltage of the power grid; is the rated current of the power grid.

[0035] By using the voltage data and current data to calculate with the sine wave signal based on the harmonic frequency, the amplitude of the corresponding frequency component is obtained, and then the harmonic content data is obtained through summation calculation. The specific formula of the harmonic content data is as follows: ; Wherein, is the harmonic content data at the th moment; is the period of the signal, that is, the time length of the complete waveform of the power grid signal; is the serial number of the harmonic frequency; is the number of harmonic frequencies to be calculated; is the voltage data at the th moment; is the current data at the th moment; is the harmonic frequency.

[0036] It should be noted that for the above methods of calculating the load volatility and harmonic content data, it can also be achieved by using methods such as the fixed empirical value method, the statistical analysis method, the signal processing method, or the machine learning method; for example, the signal processing method is combined with statistical analysis to determine the load volatility and harmonic content data. First, continuous voltage and current data are collected through voltage sensors and current sensors in the power grid, and the sampling values per minute are recorded to form time series data; then these data are preprocessed, including removing noise and outliers; then the fast Fourier transform is used to extract the harmonic frequency components and calculate the amplitudes of each frequency component, and finally the harmonic content data is obtained by summation; at the same time, by performing a sliding window analysis on the time series of voltage and current data, the load volatility within each window is calculated; to eliminate the influence of random fluctuations, the average value of multiple windows is taken as the reference load volatility, and the weight distribution of the harmonic content data is optimized through correlation analysis, and finally the calculation formulas of the load volatility and harmonic content data are determined; specifically how to determine the load volatility and harmonic content data is not specifically limited in this embodiment.

[0037] It should be noted that the existing technology usually only focuses on the performance indicators of the communication link itself, while ignoring the potential impact of the power grid load characteristics on the communication quality. This single-dimensional data acquisition method is difficult to comprehensively reflect the true state of the communication link in a complex low-voltage power grid environment, especially when the power grid load fluctuates violently or the harmonic interference is severe. In this embodiment, by introducing the load volatility and harmonic content as the core indicators, the accurate characterization of the power grid operation state is realized. Compared with the existing technology, this method can not only monitor the dynamic changes of the power grid load in real time, but also quantify the interference degree of harmonics on the communication link. This multi-dimensional data acquisition and feature extraction method solves the problems of single data source and insufficient prediction accuracy in the traditional scheme. Especially in low-voltage power grid carrier communication, load fluctuations and harmonic interference are often the main reasons for the instability of the communication link. By identifying these potential problems in advance, the reliability and anti-interference ability of the communication system can be significantly improved.

[0038] S2: Construct a channel quality model based on the power grid load characteristics and perform prediction to obtain the predicted channel quality value.

[0039] In the embodiment of the present application, the predicted channel quality value includes the predicted communication delay and the predicted packet loss rate.

[0040] In the embodiment of the present application, as Figure 2 shown in the flowchart of constructing the channel quality model, constructing a channel quality model based on the power grid load characteristics and performing prediction to obtain the predicted channel quality value means that by analyzing the power grid load characteristics, calculating the influence of the power grid load characteristics on the communication delay and the packet loss rate respectively, constructing the channel quality model, and then obtaining the predicted communication delay and the predicted packet loss rate.

[0041] Furthermore, obtaining the predicted communication delay includes the following steps: Since the load volatility directly reflects the severity of the power grid load change, and its relationship with the communication delay is usually linear. When the load volatility increases, the stability of the communication link decreases, resulting in an increase in the communication delay. Therefore, the influence of the load volatility on the communication delay is represented by a linear function, and the specific formula is as follows: ; where is the influence of the load volatility on the communication delay; is the influence coefficient of the load volatility on the communication delay, which is used to control the increase amplitude of the communication delay caused by the change of the load volatility; is the th moment of the load volatility.

[0042] Regarding the problem that the harmonic content will interfere with the stability of the power system, have a significant impact on the communication delay, and the harmonic content has a saturation effect, the influence of the harmonic content on the communication delay is introduced, and the exponential term is used to limit the unlimited increase of the communication delay caused by the high harmonic content. The specific formula is as follows: ; Wherein, is the influence of the harmonic content on the communication delay; is the influence coefficient of the harmonic content on the communication delay, indicating the increase amplitude of the communication delay caused by the harmonic content; is the harmonic content data at the moment.

[0043] Based on the reference value of the communication delay, the influences of the load volatility and the harmonic content on the delay are combined to obtain the predicted communication delay.

[0044] In the embodiment of the present application, the specific formula for predicting the communication delay is as follows: ; Wherein, is the predicted communication delay at the moment; is the reference value of the communication delay, indicating the communication delay without load fluctuation and harmonic interference; is the influence coefficient of the load volatility on the communication delay, used to control the increase amplitude of the communication delay caused by the change of the load volatility; is the load volatility at the moment; is the influence coefficient of the harmonic content on the communication delay, indicating the increase amplitude of the communication delay caused by the harmonic content; is the harmonic content data at the moment.

[0045] Furthermore, obtaining the predicted packet loss rate includes the following steps: Since the load volatility directly affects the communication quality, the greater the volatility may lead to more packet losses, and the harmonic content will affect the stability of the power grid, thereby affecting the communication quality. The fractional function is used to simulate the influence of the load volatility and the harmonic content on the packet loss rate to avoid the excessive increase of the packet loss rate. The specific formula is as follows: ; ; Wherein, is the influence of the load volatility on the packet loss rate; is the influence coefficient of the load volatility on the packet loss rate, indicating the influence of the unit load fluctuation on the increase of the packet loss rate; is the load volatility at the moment; is the influence of harmonic content on packet loss rate; is the influence coefficient of harmonic content on packet loss rate; is the harmonic content data at the

[0046] Based on the baseline value of the packet loss rate, the influences of load volatility and harmonic content on the packet loss rate are combined to obtain the predicted packet loss rate.

[0047] In the embodiment of the present application, the specific formula for predicting the packet loss rate is as follows: ; wherein, is the predicted packet loss rate at the moment; is the baseline value of the packet loss rate, indicating the packet loss rate without load fluctuation and harmonic interference; is the influence coefficient of load volatility on the packet loss rate, indicating the influence of unit load volatility on the increase of the packet loss rate; is the load volatility at the moment; is the influence coefficient of harmonic content on the packet loss rate; is the harmonic content data at the moment.

[0048] It should be noted that traditional channel quality models usually rely on a single communication metric and are difficult to adapt to the dynamic changes in a complex power grid environment. In addition, existing models often ignore the specific impacts of load volatility and harmonic content on communication quality, resulting in a large deviation between the prediction results and the actual communication status. In this embodiment, by constructing a channel quality model based on load volatility and harmonic content, the problems of low prediction accuracy and poor applicability of traditional models are solved. Among them, the relationship between load volatility and communication delay is modeled as a linear function, and the influence of harmonic content is restricted by an exponential term, which not only retains the simplicity of the model but also improves the prediction accuracy. It is particularly suitable for high-harmonic environments and avoids the distortion of model prediction results. Secondly, a fractional function is used to describe the influences of load volatility and harmonic content on the packet loss rate, avoiding the problem of excessive growth of the packet loss rate, which is particularly effective in high-load or high-harmonic environments and can significantly improve the robustness of the model.

[0049] S3: Based on the channel quality prediction value and communication quality data, node selection is performed on communication nodes to obtain backup routing nodes.

[0050] In the embodiment of the present application, as Figure 3The following shows a flowchart of node selection. Based on the predicted channel quality value and communication quality data, node selection for communication nodes includes the following steps: Make a judgment based on the predicted channel quality value and communication quality data. If the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the first cost score.

[0051] If the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the second cost score.

[0052] If the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the third cost score.

[0053] If the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, then record the routing switching cost value of the corresponding communication node as the fourth cost score.

[0054] It should be noted that for the calculation methods of the above first cost score to the fourth cost score, they can be implemented by using methods such as the fixed empirical value method, the statistical analysis method, the fuzzy logic method, or the machine learning method, etc.; in this embodiment, a method combining statistical analysis and fuzzy logic is used to determine the first cost score to the fourth cost score. First, communication node data under multiple typical power grid scenarios are selected, continuous communication delay and packet loss rate data are collected, and the real-time communication quality of each node under different load volatility and harmonic content conditions is recorded; then these data are classified and processed, and are respectively marked as four types of states: normal, slightly abnormal, moderately abnormal, and severely abnormal, and the distribution characteristics of communication delay and packet loss rate in each state are calculated through statistical analysis; then fuzzy logic rules are introduced to map the change ranges of communication delay and packet loss rate into the fuzzy set of cost scores, and finally the specific values of the first cost score to the fourth cost score are determined through a fuzzy inference system. Specifically, how to determine the first cost score to the fourth cost score is not specifically limited in this embodiment.

[0055] In the embodiment of the present application, node selection for communication nodes based on the predicted channel quality value and communication quality data further includes the following steps: Obtain all adjacent nodes of the current communication node, obtain the routing switching cost values of all adjacent nodes, compare according to the routing switching cost values of the adjacent nodes and the routing switching threshold. If the routing switching cost value of the adjacent node is less than the routing switching threshold, then classify the corresponding adjacent node as a backup routing node.

[0056] If the routing switching cost value of the adjacent node is greater than or equal to the routing switching threshold, then determine that the corresponding adjacent node is not a backup routing node.

[0057] It should be noted that for the routing switching threshold, in this example, a method combining multi-objective optimization and adaptive learning is adopted to determine the routing switching threshold: First, a multi-objective optimization model that comprehensively considers communication delay, packet loss rate, and power grid load characteristics is constructed; then, by collecting communication node data under multiple typical power grid scenarios, multiple groups of effective data samples are obtained and these data are preprocessed; then, the genetic algorithm is used to solve the optimization model to obtain a preliminary range of routing switching thresholds; finally, an adaptive learning mechanism is introduced to dynamically adjust the specific value of the threshold according to real-time communication quality and historical operation data; secondly, to ensure the robustness and applicability of the threshold, the weighted average of multiple groups of experimental data is taken as the benchmark threshold, and the threshold is optimized by the cross-validation method, and finally the routing switching threshold is determined. This method not only takes into account the advantages of multi-objective optimization in global search, but also improves the flexibility and dynamic adaptability of the threshold through adaptive learning, making the routing switching decision more scientific and reasonable; in addition, the routing switching threshold can also be determined by using methods such as the fixed empirical value method, the dynamic adjustment method, the multi-objective optimization method, or the adaptive learning method. This embodiment does not make specific limitations on this.

[0058] Exemplarily, in a low-voltage power grid carrier communication network, assume that the current communication node is Node A, and its adjacent nodes include Node B, Node C, and Node D. According to the predicted channel quality value and real-time communication quality data, if the real-time communication delay of Node B is less than or equal to the predicted communication delay and the real-time packet loss rate is less than or equal to the predicted packet loss rate, then its routing switching cost value is recorded as the first cost score; if the real-time communication delay of Node C is greater than the predicted communication delay but the packet loss rate is still less than or equal to the predicted packet loss rate, it is recorded as the second cost score; if the real-time communication delay of Node D is less than or equal to the predicted communication delay but the packet loss rate is greater than the predicted packet loss rate, it is recorded as the third cost score; finally, by comparing the routing switching cost values of each adjacent node with the routing switching threshold, the nodes below the threshold are selected as backup routing nodes, so as to realize node selection based on dynamic routing adaptability.

[0059] It should be noted that the existing routing methods are usually based on static threshold judgment, lacking comprehensive consideration of real-time communication quality and prediction values. Such a simple and crude method easily leads to frequent or untimely routing switching, thereby affecting the overall performance of the network. In this embodiment, by introducing four cost scores to classify and evaluate communication nodes, refined management of the status of communication nodes is achieved. At the same time, the design of screening backup routing nodes in combination with the routing switching threshold can minimize the routing switching frequency and reduce network overhead on the premise of ensuring communication quality. Compared with the prior art, this method can not only quickly screen out the optimal backup routing nodes, but also effectively avoid network congestion problems caused by frequent switching, which is particularly important in the case of complex power grid topologies and numerous communication nodes, significantly improving the stability and reliability of the network.

[0060] S4: Develop a routing switching strategy based on the backup routing nodes to achieve a low-voltage power line carrier communication network formation based on dynamic routing adaptation.

[0061] In the embodiment of the present application, developing a routing switching strategy based on the backup routing nodes includes the following steps: Obtain the load volatility and harmonic content data of the backup routing nodes based on all the backup routing nodes of the current communication node, calculate the load correlation coefficient between the current communication node and the backup routing nodes according to the load volatility and harmonic content data of the backup routing nodes. The specific formula for the load correlation coefficient is as follows: ; Wherein, is the load correlation coefficient between the current communication node and the th backup routing node, that is, the load correlation coefficient between the th communication node and the th backup routing node; is the number of sampling points; is the load volatility of the current communication node at the th moment; is the average value of the load volatility of the current communication node; is the load volatility of the th backup routing node at the th moment; is the average value of the load volatility of the th backup routing node; is the harmonic content data of the current communication node at the th moment; is the average value of the harmonic content data of the current communication node; is the harmonic content data of the th backup routing node at the th moment; is the The mean value of the harmonic content data of one backup routing node.

[0062] Compare the load correlation coefficients of all backup routing nodes, and use the backup routing node with the lowest load correlation coefficient as the target routing node.

[0063] Establish a communication link between the current communication node and the target routing node, and disconnect the original communication link to achieve a low-voltage power grid carrier communication network based on dynamic routing adaptation.

[0064] In an optional embodiment, the calculation of the load correlation coefficient can be achieved in various ways, not limited to the formulaic method. For example, by training a regression or classification model (such as a neural network, random forest, etc.), using the load volatility and harmonic content data of the current communication node and the backup routing node as input features, and outputting the correlation score between the two; or the distance metric can be used to measure the load correlation between the current communication node and the backup routing node. This embodiment does not make specific limitations on this.

[0065] It should be noted that traditional routing switching strategies usually only consider the quality of the communication link and ignore the load correlation between nodes, which easily leads to communication link overload or uneven load distribution, thus affecting the overall performance of the network. In this embodiment, by calculating the load correlation coefficient between the current communication node and the backup routing node, a quantitative evaluation of the load synergy effect between nodes is realized. Among them, selecting the backup routing node with the lowest load correlation as the target routing node can effectively disperse the power grid load pressure and avoid the problem of communication link overload caused by load concentration. Compared with the prior art, this strategy can not only achieve dynamic optimization of the communication link, but also improve the anti-interference ability and self-adaptive ability of the network. Especially in the case of uneven power grid load distribution or local overload, this strategy can significantly improve the overall performance of the network.

[0066] In summary, the present invention simultaneously collects power grid load data such as voltage and current and communication quality data such as communication delay and packet loss rate, and extracts key features such as load volatility and harmonic content, establishing an association basis between the power grid operation state and communication performance for the system; by constructing a channel quality model, accurately describing the influence mechanism of load characteristics on communication performance, and achieving accurate prediction of the change trend of channel quality; based on the comparative analysis of the predicted value and the real-time value, establishing a four-level routing switching cost evaluation mechanism, screening backup nodes in combination with the routing switching threshold, effectively avoiding the instability caused by frequent switching while ensuring the flexibility of the communication network; by introducing load correlation analysis, selecting the optimal target routing node for link switching, significantly improving the load balancing effect; not only solving the problems of fixed routing strategy and insufficient channel quality prediction ability in traditional low-voltage power grid carrier communication, but also realizing the dynamic optimization and adaptive adjustment of the communication network through the combination of multi-dimensional data analysis and intelligent decision-making mechanism, effectively improving the reliability, real-time performance and adaptability of the communication network, and providing a reliable communication guarantee for the data acquisition and distribution automation services of smart grids.

[0067] Embodiment 2 is an embodiment of the present invention, which provides a low-voltage power grid carrier communication networking system with dynamic routing adaptability, including: a data acquisition module, configured to acquire power grid load data and communication quality data, extract features from the power grid load data to obtain power grid load characteristics; a model construction module, configured to construct a channel quality model based on the power grid load characteristics and perform prediction to obtain a channel quality prediction value; a node selection module, configured to perform node selection on communication nodes based on the channel quality prediction value and the communication quality data to obtain backup routing nodes; a routing switching module, configured to formulate a routing switching strategy according to the backup routing nodes to implement low-voltage power grid carrier communication networking based on dynamic routing adaptability.

[0068] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that: As Figure 4As shown, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0069] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0070] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0071] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A low-voltage power grid carrier communication networking method with dynamic routing self-adaptation, characterized in that: include: Acquire power grid load data and communication quality data, perform feature extraction on the power grid load data, and obtain power grid load features; Constructing a channel quality model based on the power grid load characteristics and performing prediction to obtain a channel quality prediction value; Perform node selection on a communication node based on the channel quality prediction value and the communication quality data to obtain a backup routing node; A routing switching strategy is formulated according to the backup routing node to realize low-voltage power grid carrier communication networking based on dynamic routing adaptation.

2. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation according to claim 1, characterized in that: The grid load data includes voltage data and current data; The communication quality data includes communication delay and packet loss rate; The grid load characteristics include load fluctuation rate and harmonic content data.

3. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation according to claim 2, characterized in that: The channel quality prediction value includes a predicted communication delay and a predicted packet loss rate; Constructing a channel quality model based on the power grid load characteristics and making predictions to obtain a channel quality prediction value means analyzing the power grid load characteristics, calculating the impact of the power grid load characteristics on communication delay and packet loss rate, constructing a channel quality model, and then obtaining the predicted communication delay and predicted packet loss rate.

4. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation as claimed in claim 3, characterized in that: Obtaining the predicted communication delay includes the following steps: The influence of load fluctuation rate on communication delay is expressed by using a linear function. The specific formula is as follows: ； in, The impact of load fluctuation rate on communication delay; is the influence coefficient of load fluctuation rate on communication delay; For the Load fluctuation rate at each moment; The influence of harmonic content on communication delay is introduced, and the exponential term is used to limit the unlimited increase of communication delay caused by high harmonic content. The specific formula is as follows: ； in, The influence of harmonic content on communication delay; is the influence coefficient of harmonic content on communication delay; For the Harmonic content data at the time; Based on the baseline value of communication delay, the influence of load fluctuation rate and harmonic content on delay is combined to obtain the predicted communication delay; The specific formula for predicting the communication delay is as follows: ； in, For the Predicted communication delay at each moment; is the baseline value of communication delay; is the influence coefficient of load fluctuation rate on communication delay; For the Load fluctuation rate at each moment; is the influence coefficient of harmonic content on communication delay; For the Harmonic content data at the time.

5. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation according to claim 4, characterized in that: Obtaining the predicted packet loss rate includes the following steps: The influence of load fluctuation rate and harmonic content on packet loss rate is simulated by using fractional function. The specific formula is as follows: ； ； in, The impact of load fluctuation on packet loss rate; is the influence coefficient of load fluctuation rate on packet loss rate; For the Load fluctuation rate at each moment; is the effect of harmonic content on packet loss rate; is the influence coefficient of harmonic content on packet loss rate; For the Harmonic content data at the time; Based on the baseline value of packet loss rate, the influence of load fluctuation rate and harmonic content on packet loss rate is combined to obtain the predicted packet loss rate; The specific formula for predicting the packet loss rate is as follows: ； in, For the Predicted packet loss rate at the time; is the baseline value of packet loss rate; is the influence coefficient of load fluctuation rate on packet loss rate; For the Load fluctuation rate at each moment; is the influence coefficient of harmonic content on packet loss rate; For the Harmonic content data at the time.

6. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation according to claim 5, characterized in that: Selecting a communication node based on the channel quality prediction value and the communication quality data comprises the following steps: Based on the channel quality prediction value and the communication quality data, if the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, the route switching cost value of the corresponding communication node is recorded as the first cost score; If the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is less than or equal to the predicted packet loss rate, the route switching cost value of the corresponding communication node is recorded as the second cost score; If the real-time communication delay is less than or equal to the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, the route switching cost value of the corresponding communication node is recorded as the third cost score; If the real-time communication delay is greater than the predicted communication delay, and the real-time packet loss rate is greater than the predicted packet loss rate, the route switching cost value of the corresponding communication node is recorded as the fourth cost score; Performing node selection on a communication node based on the channel quality prediction value and the communication quality data further comprises the following steps: Obtain all neighboring nodes of the current communication node, obtain the routing switching cost values ​​of all neighboring nodes, compare the routing switching cost values ​​of the neighboring nodes with the routing switching threshold, and if the routing switching cost value of the neighboring node is less than the routing switching threshold, classify the corresponding neighboring node as a backup routing node; If the routing switching cost value of the adjacent node is greater than or equal to the routing switching threshold, it is determined that the corresponding adjacent node is not a backup routing node.

7. The method for low-voltage power grid carrier communication networking with dynamic routing self-adaptation according to claim 6, characterized in that: Formulating a routing switching strategy according to the backup routing node includes the following steps: Based on all backup routing nodes of the current communication node, the load fluctuation rate and harmonic content data of the backup routing node are obtained, and the load correlation coefficient between the current communication node and the backup routing node is calculated according to the load fluctuation rate and harmonic content data of the backup routing node; Compare the load correlation coefficients of all backup routing nodes, and use the backup routing node with the lowest load correlation coefficient as the target routing node; A communication link is established between the current communication node and the target routing node, and the original communication link is disconnected.

8. A dynamic routing adaptive low-voltage power grid carrier communication networking system, based on the dynamic routing adaptive low-voltage power grid carrier communication networking method according to any one of claims 1 to 7, characterized in that: include, A data acquisition module is used to acquire power grid load data and communication quality data, perform feature extraction on the power grid load data, and obtain power grid load features; A model building module is used to build a channel quality model based on power grid load characteristics and make predictions to obtain a channel quality prediction value; A node selection module, used to select a communication node based on a channel quality prediction value and communication quality data to obtain a backup routing node; The routing switching module is used to formulate a routing switching strategy according to the backup routing nodes to realize low-voltage power grid carrier communication networking based on dynamic routing adaptation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the dynamic routing adaptive low-voltage power grid carrier communication networking method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dynamic routing adaptive low-voltage power grid carrier communication networking method described in any one of claims 1 to 7 are implemented.

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