Network optimization method for power line carrier communication system

Abstract

The invention relates to the technical field of network optimization, in particular to a network optimization method for a power line carrier communication system, which comprises the following steps of: intelligently dividing communication areas to ensure the optimal communication quality among nodes in the same communication area and reduce signal interference and attenuation; impedance matching is completed in the same communication area, channel attenuation is reduced, network configuration is dynamically adjusted according to a network link communication quality matrix, and it is ensured that the network is always in the optimal state; by optimizing and reconfiguring the proxy sites in the industrial automation network, the delay and loss in the data transmission process can be reduced, so that the expansibility and flexibility of the network are ensured; the overall efficiency and stability of the network are improved by analyzing the network hierarchical structure and the traffic demand, formulating a load balancing strategy, optimizing the network hierarchy and reducing unnecessary nodes and links. The invention is used for solving the technical problem of insufficient optimization of industrial automation network communication in the existing scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of network optimization, and in particular to a network optimization method for a power line carrier communication system. Background Art

[0002] Network optimization technology is the process of collecting parameters and analyzing data of a running network to find out the reasons that affect the network quality and take improvement measures to make the network reach the best operating state. Power line carrier communication is a communication method that uses power lines as a transmission medium for data transmission.

[0003] With the continuous advancement of science and technology, network optimization technology has shown a wide range of application value in many fields, including the Internet of Things and smart cities, the Internet and media, etc. It also plays a vital role in the field of industrial automation. However, the current network communication in the field of industrial automation, especially in the environment of power line carrier communication system, still faces a series of challenges: unstable communication quality. In the industrial automation network, when the device nodes transmit data through the power line carrier communication system, signal interference and attenuation problems are often encountered, resulting in poor communication quality and affecting the reliability and stability of data transmission; the network configuration lacks flexibility. The existing power line carrier communication network configuration is difficult to quickly adapt to the changes in the industrial automation network environment. There is a lack of dynamic adjustment and optimization mechanism, which leads to network instability. Performance bottleneck of proxy sites. When proxy sites in industrial automation networks use power line carrier communication for data transmission, there are delays and losses, which directly limit the scalability and flexibility of the network. The network hierarchy is complex. The current industrial automation network, especially the network using power line carrier communication system, has a complex hierarchy with unnecessary nodes and links, which increases the complexity and redundancy of the network and reduces the stability and reliability of the network. Load imbalance. In the power line carrier communication system, due to the imbalance of data transmission between nodes, some nodes are overloaded, which seriously affects the overall efficiency and stability of the network. In response to these challenges, corresponding solutions and technical means are needed to improve the efficiency and quality of network communication in the field of industrial automation. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a network optimization method for a power line carrier communication system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A network optimization method for a power line carrier communication system, comprising:

[0007] Step 1: Perform intelligent communication area division and impedance matching in the industrial automation network, and dynamically adjust the industrial automation network;

[0008] Step 2: Optimize and reconfigure proxy sites in the industrial automation network;

[0009] Step 3: Analyze the hierarchical structure and traffic requirements in the industrial automation network to develop a load balancing strategy.

[0010] Furthermore, the process of dividing the intelligent communication areas in the industrial automation network includes:

[0011] Set N device nodes in the industrial automation network, and device node i contains the device node position coordinates NC i =(x i ,y i ) and the communication radius r i ; Where N represents the number of device nodes;

[0012] A clustering algorithm is used to divide the communication area, and N device nodes are divided into K communication areas, so that the communication quality of nodes in each communication area is optimized; wherein, the clustering algorithm formula is:

[0013]

[0014] Where F(R) represents the communication area partition function value; R represents the communication area partition set, and R = {R1, R2, ..., R K}; R k represents the kth divided communication area; K represents the number of communication areas obtained after the communication area division is completed; k represents the communication area index obtained after the communication area division is completed; D(NC i ,NC j ) represents the distance between device node i and device node j, which is used to ensure that the distance between device nodes in the same communication area is relatively close, thereby optimizing the communication quality; r i 、r j Respectively represent the communication radius of the i-th and j-th device nodes; i and j represent the device node index; NC j Indicates the location coordinates of device node j.

[0015] Furthermore, the process of performing impedance matching in the industrial automation network and dynamically adjusting the industrial automation network includes:

[0016] In the same communication area, the channel impedance value between device nodes is marked as Z ij ;

[0017] By adjusting the channel impedance value, the channel attenuation can be reduced to a minimum. The formula for adjusting the channel impedance value is:

[0018]

[0019] Where L(Z) represents the minimized channel attenuation function; Z represents the impedance matrix; Z' ij represents the target impedance value; wherein the purpose of the formula for adjusting the channel impedance value is to obtain the minimum value from the impedance matrix Z to minimize the channel attenuation;

[0020] Get the industrial automation network link communication quality matrix Q = (Q ij ), where Q ij Indicates the communication quality between device node i and device node j;

[0021] It can be understood that in industrial automation networks, communication quality refers to the reliability and efficiency of data transmission between different device nodes; it is represented by a matrix Q, where each element Q ij It represents the communication quality between device node i and device node j, so the matrix Q is regarded as the matrix of industrial automation network link communication quality. In practical applications, it is necessary to evaluate multiple factors through measurement, simulation or mathematical modeling, including data transmission rate, error rate, delay, jitter, etc., and calculate the value of each element of the Q matrix accordingly. Then, the value of each element in the Q matrix is ​​observed to find out the device nodes with poor communication quality, and then take corresponding optimization measures to improve the communication quality of the entire network.

[0022] Regularly update the industrial automation network link communication quality matrix Q, according to the new industrial automation network link communication quality matrix Q (τ+1) Dynamically adjust the industrial automation network; Q (τ+1) =Q (τ) +ΔQ, where τ represents the number of updates; Q (τ+1) represents the updated industrial automation network link communication quality matrix; Q (τ) represents the current industrial automation network link communication quality matrix; ΔQ represents the increment of the industrial automation network link communication quality matrix, where the increment is calculated based on new data measured by the network, adjustment of regional division, optimization of impedance matching, and other factors;

[0023] It is understandable that in industrial automation networks, dynamic adjustments may involve multiple aspects, including but not limited to area division, load and channel; the present invention obtains corresponding adjustments by updating the matrix Q; the new data based on network measurement refers to the results of network measurement, including key performance indicators such as network delay, packet loss rate, and bandwidth utilization.

[0024] Furthermore, the process of optimizing and reconfiguring the proxy sites in the industrial automation network includes:

[0025] Statistically obtain the proxy site set A of the industrial automation network, the lth proxy site A l There is a proxy site location vector PC l and communication capabilities l ; where l represents the proxy site index, and the communication capacity c l It is a quantitative indicator used to measure the performance of proxy sites in forwarding and aggregating data;

[0026] Obtain the average delay Tavg, proxy site load LD, and network link information NL for all data packet transmissions in the industrial automation network;

[0027] The overall performance of the proxy site is quantitatively analyzed using the performance evaluation function g(A); the formula of the performance evaluation function g(A) is:

[0028]

[0029] In the formula, It represents the reverse index of the industrial automation network communication efficiency. If the reverse index value of the industrial automation network communication efficiency is higher, the industrial automation network communication efficiency is higher. Tavg represents the average delay of all data packet transmission in the industrial automation network. It represents the proxy site load balancing index. When the proxy site load balancing index is closer to 1, the load balancing degree is higher. σLD represents the standard deviation of the proxy site load. LD(max) represents the maximum value of the proxy site load. represents the redundancy index of the industrial automation network. If the redundancy index value of the industrial automation network is higher, the stability and recovery ability of the industrial automation network in the event of a fault will be stronger; μ1(NL) represents the number of redundant links or nodes in the network link information NL; μ2(NL) represents the number of total links or nodes in the network link information NL; α, β, and γ are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively, which are used to adjust the relative importance of different evaluation indicators in the performance evaluation function;

[0030] Combined with the performance evaluation function g(A), the optimal proxy site is selected from the candidate proxy site set A' for reconfiguration: In the formula, A 0 represents the optimal proxy site set, that is, the proxy site set selected from the candidate site set A' that makes the performance evaluation function g(A) reach the maximum value; argmax is an operator used to find the proxy site set A that makes the performance evaluation function g(A) reach the maximum value;

[0031] According to the reconfiguration results of the proxy site set, the optimal proxy site location vector is determined and communication capabilities

[0032] Furthermore, the process of analyzing the hierarchical structure in the industrial automation network includes:

[0033] Use the hierarchical analysis method to analyze the hierarchical structure in the industrial automation network: establish a hierarchical structure for the industrial automation network and divide the industrial automation network into H levels; each level contains n h devices, h represents the index of the hierarchical structure, h = 1, 2, ..., H, H represents the total number of divided levels;

[0034] It is understandable that the industrial automation network includes multiple layers, each of which has different functions and tasks. The specific industrial automation network layer structure includes the field device layer, the control layer, and the information management layer;

[0035] For level h, establish an n h ×n h Judgment Matrix B h , used to compare the communication requirements between device nodes in level h; where the judgment matrix B h Elements in Indicates the degree of communication demand of device u relative to device v, and devices u and v correspond to judgment matrix B h The u-row elements and v-row elements of ;

[0036] It can be understood that the degree of communication demand refers to the urgency or size of the need for data exchange or information transmission between different device nodes in the power line carrier communication system. In order to quantify this demand, a specific mathematical tool, namely the judgment matrix, is used; by constructing the judgment matrix, the degree of communication demand between devices at different levels in the network can be quantitatively analyzed;

[0037] For the judgment matrix B h Normalize and get the weight vector w h ; Among them, the normalization formula is:

[0038]

[0039] In the formula, represents the weight of device u in level h; Represents the judgment matrix B h The sum of the elements in the uth row and the sum of the elements in the vth row;

[0040] Calculate the judgment matrix B h The consistency index CIh : In the formula, Represents the judgment matrix B h The maximum eigenvalue, consistency index CI h Used to measure the consistency of the judgment matrix, n h Represents the judgment matrix B h The size of the judgment matrix B h There are h Row and n h List;

[0041] Get the random consistency index RI h , this indicator depends on the judgment matrix B h n h , used to calculate the consistency ratio CR h ; Among them, the random consistency index RI h is obtained by referring to the RI value table;

[0042] Based on the judgment matrix B h The consistency index CI h and random consistency index RI h To perform a consistency check: In the formula, CR h Indicates the consistency ratio. If CR h <0.1, then it is considered that the judgment matrix B h Be consistent;

[0043] According to the weight vector w h And the judgment matrix B h , determine the communication path corresponding to the device at level h;

[0044] It is understandable that once the judgment matrix B h is confirmed to be consistent, then according to the weight vector w h And the judgment matrix B h Determine the communication path corresponding to the device at level h, that is, select the device with higher weight as the transit point of communication or the path with higher priority for data transmission.

[0045] Furthermore, the process of analyzing the traffic demand in the industrial automation network and formulating a load balancing strategy includes:

[0046] The communication paths between device nodes in the industrial automation network are regarded as edges, and an industrial automation network flow graph G = (V, E) is constructed, where V represents the device node set and E represents the edge set; G = (V, E) is a directed graph, that is, a graph composed of the device node set V and the edge set E, which represents all device nodes and communication paths in the industrial automation network;

[0047] For any two device nodes i and j, establish the traffic balance equation:

[0048]

[0049] In the formula, f ij represents the actual traffic from device node i to device node j, that is, the actual traffic of edge E(i,j), f ji represents the actual flow from device node j to device node i, that is, the actual flow of edge E(j,i); wherein the source node and sink node are not considered when establishing the flow balance equation;

[0050] Among them, the load balancing strategy specifically includes: dynamically adjusting the communication path to balance the traffic, adding redundant links to increase network capacity, using load balancing equipment, etc.; using load balancing strategy to disperse the traffic to multiple device nodes to avoid overloading a single device node.

[0051] Compared with the existing technology, the present invention provides a method for optimizing the network of a power line carrier communication system, which has the following advantages:

[0052] 1. The present invention ensures that the communication quality between device nodes in the same communication area is optimized by performing intelligent communication area division and impedance matching in the industrial automation network and dynamically adjusting the industrial automation network. The intelligent communication area division can reduce channel interference and attenuation, thereby improving the communication efficiency of the entire industrial automation network. By adjusting the channel impedance value, the signal loss during the transmission process is minimized, thereby increasing the channel transmission capacity and improving the communication quality. By regularly updating and dynamically adjusting the industrial automation network link communication quality matrix, it helps to enhance the flexibility and adaptability of the industrial automation network, and can effectively respond to changes in the industrial automation network environment, ensuring that the industrial automation network is always in the best state.

[0053] 2. The present invention can reduce the level of the industrial automation network and shorten the link length by optimizing and reconfiguring the proxy sites in the industrial automation network, thereby reducing the delay and loss in the data transmission process, helping to improve the reliability and stability of data transmission, and at the same time making it easier to expand the scale of the industrial automation network. When a new device node joins the industrial automation network, it can quickly find a suitable proxy site for connection, thereby ensuring the scalability and flexibility of the industrial automation network; by quantitatively analyzing the overall performance of the proxy site through the performance evaluation function, the optimal proxy site set can be selected, thereby ensuring the efficient use of industrial automation network resources;

[0054] 3. The present invention reduces unnecessary nodes and links by analyzing the hierarchical structure and traffic requirements in the industrial automation network, thereby reducing the complexity and redundancy of the industrial automation network, helping to enhance the stability and reliability of the industrial automation network and reducing the probability of industrial automation network failures. By formulating a load balancing strategy and dynamically adjusting the load of each device node, it helps to ensure the rational use of resources, improve resource utilization, and thus enhance the overall performance of the industrial automation network.

[0055] In summary, the present invention comprehensively optimizes the network structure of the power line carrier communication system by comprehensively applying the three steps of intelligent communication area division and impedance matching, proxy site optimization and reconfiguration, and network level optimization and load balancing. The method improves communication efficiency, data transmission quality and network stability, provides a more reliable and efficient solution for network communication in the field of industrial automation, and ensures the normal implementation of a subsequent network optimization method for a power line carrier communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The present invention provides a flow chart of a method for optimizing a power line carrier communication system network. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Reference Figure 1 , a network optimization method for a power line carrier communication system, comprising:

[0059] Step 1: Perform intelligent communication area division and impedance matching in the industrial automation network, and dynamically adjust the industrial automation network;

[0060] Step 2: Optimize and reconfigure proxy sites in the industrial automation network;

[0061] Step 3: Analyze the hierarchical structure and traffic requirements in the industrial automation network to develop a load balancing strategy.

[0062] It should be noted that the network optimization method for a power line carrier communication system proposed in the present invention can be widely applied to network communication optimization in the field of industrial automation. The method can be used to improve communication efficiency, data transmission quality and network stability. Specifically, it can be through intelligent communication area division and impedance matching, proxy site optimization and reconfiguration, and network hierarchy optimization and load balancing. The three technologies of communication area division, proxy site performance, network hierarchy structure and traffic demand in the industrial automation network are comprehensively analyzed and accurately evaluated, ensuring that the communication quality between device nodes in the same communication area is optimal, improving the overall network performance, and promoting the flexibility and adaptability of the industrial automation network; in summary, the network optimization method for a power line carrier communication system proposed in the present invention not only meets the requirements of the industrial automation field for efficient, stable and reliable network communication, but also provides strong support for the flexibility and adaptability of the industrial automation network through intelligent and precise optimization means, and at the same time provides a more reliable and efficient solution for network communication in the field of industrial automation, which has broad application prospects and important practical value.

[0063] See also Figure 1 The present invention provides a method for optimizing a power line carrier communication system network. In step 1, the steps of performing intelligent communication area division and impedance matching in an industrial automation network and dynamically adjusting the industrial automation network include:

[0064] Step 101: Set N device nodes in the industrial automation network, and the device node i contains the device node position coordinates NC i =(x i ,y i ) and communication radius r i ; Where N represents the number of device nodes;

[0065] Step 102: Use a clustering algorithm to divide the communication area, divide the N device nodes into K communication areas, and optimize the communication quality of the nodes in each communication area; wherein the clustering algorithm formula is:

[0066]

[0067] Where F(R) represents the communication area partition function value; R represents the communication area partition set, and R = {R1, R2, ..., R K}; R k represents the kth divided communication area; K represents the number of communication areas obtained after the communication area division is completed; k represents the communication area index obtained after the communication area division is completed; D(NC i ,NC j) represents the distance between device node i and device node j, which is used to ensure that the distance between device nodes in the same communication area is relatively close, thereby optimizing the communication quality; r i 、r j Respectively represent the communication radius of the i-th and j-th device nodes; i and j represent the device node index; NC j Indicates the location coordinates of device node j;

[0068] Step 103: In the same communication area, the channel impedance value between the device nodes is marked as Z. ij ;

[0069] Step 104: Reduce the channel attenuation to a minimum by adjusting the channel impedance value; wherein the formula for adjusting the channel impedance value is:

[0070]

[0071] Where L(Z) represents the minimized channel attenuation function; Z represents the impedance matrix; Z' ij represents the target impedance value; wherein the purpose of the formula for adjusting the channel impedance value is to obtain the minimum value from the impedance matrix Z to minimize the channel attenuation;

[0072] Step 105: Obtain the industrial automation network link communication quality matrix Q = (Q ij ), where Q ij Indicates the communication quality between device node i and device node j;

[0073] In step 105, in an industrial automation network, communication quality refers to the reliability and efficiency of data transmission between different device nodes; it is represented by a matrix Q, where each element Q ij It represents the communication quality between device node i and device node j, so the matrix Q is regarded as the matrix of industrial automation network link communication quality; in practical applications, it is necessary to evaluate multiple factors through measurement, simulation or mathematical modeling, among which multiple factors include data transmission rate, error rate, delay, jitter, etc., and calculate the value of each element of the Q matrix accordingly; then observe the value of each element in the Q matrix, find out the device nodes with poor communication quality, and then take corresponding optimization measures to improve the communication quality of the entire network

[0074] Step 106: Regularly update the industrial automation network link communication quality matrix Q, and (τ+1) Dynamically adjust the industrial automation network; Q (τ+1) =Q (τ) +ΔQ, where τ represents the number of updates; Q (τ+1)represents the updated industrial automation network link communication quality matrix; Q (τ) represents the current industrial automation network link communication quality matrix; ΔQ represents the increment of the industrial automation network link communication quality matrix, where the increment is calculated based on new data measured by the network, adjustment of regional division, optimization of impedance matching, and other factors;

[0075] In step 106, in the industrial automation network, dynamic adjustment may involve multiple aspects, including but not limited to regional division, load and channel; the present invention obtains corresponding adjustments by updating the matrix Q; the new data based on network measurement refers to the results of network measurement, including key performance indicators such as network delay, packet loss rate, bandwidth utilization, etc.

[0076] In steps 101-106, the present invention introduces an innovative network optimization method in view of the characteristics of widespread distribution of equipment and complex communication requirements in industrial automation environments, as well as the inherent properties of power line carrier communication systems; the method first uses intelligent area division technology to reasonably allocate equipment to different communication areas, so as to optimize the communication quality of the power line carrier communication system; in the same communication area, an impedance matching strategy is implemented to improve the channel attenuation characteristics and thereby improve communication efficiency; in addition, the present invention also compares the communication quality matrix of new and old industrial automation network links, so that network administrators can accurately identify links or nodes with declining communication quality. Based on these insights, administrators can quickly take targeted measures for optimization, thereby significantly improving the overall network performance of the power line carrier communication system.

[0077] See also Figure 1 The present invention provides a method for optimizing a power line carrier communication system network. In step 2, the step of optimizing and reconfiguring the proxy site in the industrial automation network includes:

[0078] Step 201: Obtain a set of proxy sites A of the industrial automation network through statistics. The lth proxy site A l There is a proxy site location vector PC l and communication capabilities l ; where l represents the proxy site index, and the communication capacity c l It is a quantitative indicator used to measure the performance of proxy sites in forwarding and aggregating data;

[0079] For example, suppose there are two proxy sites U1 and U2; the hardware configuration of proxy site U1 is high, the software is well optimized, but the current network load is heavy; while the hardware configuration of proxy site U2 is average, but the network load is light, and the software is well optimized; after scoring and weighted averaging the above factors, the communication capacity c of U1 is obtained. l is 8 points, while U2's communication capability cl The score is 7.5; therefore, U1 performs better in forwarding and aggregating data;

[0080] Step 202, obtaining the average delay Tavg, proxy site load LD and network link information NL of all data packet transmissions in the industrial automation network;

[0081] Step 203: Use the performance evaluation function g(A) to quantitatively analyze the overall performance of the proxy site; wherein the formula of the performance evaluation function g(A) is:

[0082]

[0083] In the formula, It represents the reverse index of the industrial automation network communication efficiency. If the reverse index value of the industrial automation network communication efficiency is higher, the industrial automation network communication efficiency is higher. Tavg represents the average delay of all data packet transmission in the industrial automation network. It represents the proxy site load balancing index. When the proxy site load balancing index is closer to 1, the load balancing degree is higher. σLD represents the standard deviation of the proxy site load. LD(max) represents the maximum value of the proxy site load. represents the redundancy index of the industrial automation network. If the redundancy index value of the industrial automation network is higher, the stability and recovery ability of the industrial automation network in the event of a fault will be stronger; μ1(NL) represents the number of redundant links or nodes in the network link information NL; μ2(NL) represents the number of total links or nodes in the network link information NL; α, β, and γ are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively, which are used to adjust the relative importance of different evaluation indicators in the performance evaluation function;

[0084] Step 204: Select the best proxy site from the candidate proxy site set A' for reconfiguration in combination with the performance evaluation function g(A): In the formula, A 0 represents the optimal proxy site set, that is, the proxy site set selected from the candidate site set A' that makes the performance evaluation function g(A) reach the maximum value; argmax is an operator used to find the proxy site set A that makes the performance evaluation function g(A) reach the maximum value;

[0085] Step 205: Determine the optimal proxy site location vector according to the reconfiguration result of the proxy site set. and communication capabilities

[0086] In steps 201-205, for the power line carrier communication system, optimizing the location of the proxy site is used as a key method for network optimization; specifically, by carefully planning the layout of the proxy site, the industrial automation network level can be effectively reduced, and the length of the communication link on the power line can be shortened, thereby significantly improving the quality and efficiency of data transmission in the power line carrier communication system.

[0087] See also Figure 1 The present invention provides a network optimization method for a power line carrier communication system. In step three, the step of analyzing the hierarchical structure and flow requirements in the industrial automation network to formulate a load balancing strategy includes:

[0088] Step 301: Analyze the hierarchical structure in the industrial automation network using the hierarchical analysis method, establish a hierarchical structure for the industrial automation network, and divide the industrial automation network into H levels; each level contains n h devices, h represents the index of the hierarchical structure, h = 1, 2, ..., H, H represents the total number of divided levels;

[0089] In step 301, the industrial automation network includes multiple layers, each layer undertakes different functions and tasks, and the specific industrial automation network layer structure includes a field device layer, a control layer, and an information management layer;

[0090] Step 302: For level h, establish an n h ×n h Judgment Matrix B h , used to compare the communication requirements between device nodes in level h; where the judgment matrix B h Elements in Indicates the degree of communication demand of device u relative to device v, and devices u and v correspond to judgment matrix B h The u-row elements and v-row elements of ;

[0091] In step 302, the communication demand degree refers to the urgency or demand size of data exchange or information transmission between different device nodes in the power line carrier communication system. In order to quantify this demand, a specific mathematical tool, namely, a judgment matrix, is used; by constructing the judgment matrix, the communication demand degree between devices at different levels in the network can be quantitatively analyzed;

[0092] Step 303: judge the matrix B h Normalize and get the weight vector w h ; Among them, the normalization formula is:

[0093]

[0094] In the formula, represents the weight of device u in level h; Represents the judgment matrix B h The sum of the elements in the uth row and the sum of the elements in the vth row;

[0095] Step 304: Calculate the judgment matrix B h The consistency index CI h : In the formula, Represents the judgment matrix B h The maximum eigenvalue, consistency index CI h Used to measure the consistency of the judgment matrix, n h Represents the judgment matrix B h The size of the judgment matrix B h There are h Row and n h List;

[0096] Step 305: Obtain random consistency index RI h , this indicator depends on the judgment matrix B h n h , used to calculate the consistency ratio CR h ; Among them, the random consistency index RI h is obtained by referring to the RI value table;

[0097] Step 306: Based on the judgment matrix B h The consistency index CI h and random consistency index RI h To perform a consistency check: In the formula, CR h Indicates the consistency ratio. If CR h <0.1, then it is considered that the judgment matrix B h Be consistent;

[0098] Step 307: According to the weight vector w h And the judgment matrix B h , determine the communication path corresponding to the device at level h;

[0099] In step 307, once the matrix B h is confirmed to be consistent, then according to the weight vector w h And the judgment matrix B h Determine the communication path corresponding to the device at level h, that is, select the device with higher weight as the transit point of communication or the path with higher priority for data transmission;

[0100] Step 308: Consider the communication paths between device nodes in the industrial automation network as edges, and construct an industrial automation network flow graph G = (V, E), where V represents a set of device nodes and E represents a set of edges;

[0101] In step 308, G = (V, E) is a directed graph, i.e., a graph consisting of a device node set V and an edge set E, representing all device nodes and communication paths in the industrial automation network;

[0102] Step 309: For any two device nodes i and j, establish a flow balance equation:

[0103]

[0104] In the formula, f ij represents the actual traffic from device node i to device node j, that is, the actual traffic of edge E(i,j), f ji represents the actual flow from device node j to device node i, that is, the actual flow of edge E(j,i);

[0105] In step 309, source nodes and sink nodes are not considered when establishing the traffic balance equation; wherein the load balancing strategy specifically includes: dynamically adjusting the communication path to balance the traffic, adding redundant links to increase network capacity, using load balancing equipment, etc.; using the load balancing strategy to distribute the traffic to multiple device nodes to avoid overloading a single device node;

[0106] In steps 308-309, a load balancing strategy is implemented for network optimization of the power line carrier communication system; the load balancing strategy can dynamically adjust the load distribution of each device node according to the real-time status of the network traffic in the power line carrier communication system; this method ensures the rational use of power line carrier communication resources, which not only improves resource utilization, but also improves the overall performance of the entire industrial automation network; at the same time, by optimizing load balancing, the security of the power line carrier communication system is further enhanced, providing a solid guarantee for the smooth progress of the production process, and meeting the strict real-time requirements of the industrial automation network.

[0107] In the embodiment of the present invention, by intelligent communication area division, a clustering algorithm is used to divide device nodes into different communication areas, ensuring that the distance between device nodes in the same communication area is relatively close, thereby optimizing communication quality, helping to reduce signal attenuation and interference, and improving the reliability and stability of data transmission. By performing impedance matching in the same communication area and adjusting the channel impedance value to reduce channel attenuation to a minimum, it helps to increase channel transmission capacity and improve communication efficiency. By regularly updating the industrial automation network link communication quality matrix and dynamically adjusting and optimizing the industrial automation network according to the new quality matrix, it helps to promptly discover and solve the problem of communication quality degradation and ensure that the network is always in the best state. By evaluating the performance and location of existing proxy sites in the industrial automation network and selecting the optimal proxy site from the candidate sites for reconfiguration, it helps to reduce Low network level, shorten link length, thus improving data transmission quality, by optimizing the configuration of proxy sites, network resources can be used more effectively, resource utilization can be improved, and operating costs can be reduced. By analyzing the hierarchical structure and traffic demand in the industrial automation network and formulating a load balancing strategy, the traffic is dispersed to multiple nodes, which helps to avoid single node overload and improve the overall efficiency and stability of the network. The load balancing strategy can dynamically adjust the load of each device node according to the actual situation of network traffic, ensure the rational use of resources, help improve resource utilization, and meet the real-time requirements of the industrial automation network. At the same time, by optimizing the network level and load balancing strategy, the risk points in the industrial automation network can be reduced, the security of the network can be enhanced, and it helps to ensure the smooth progress of the industrial production process, avoiding production interruptions or data loss caused by network failures. In summary, the examples of the present invention involve data processing, comprehensive analysis and intelligent optimization decisions, and solve the technical problems of insufficient optimization of industrial automation network communication in existing solutions. In actual situations, more data and context information may be needed to make specific decisions and optimization plans.

[0108] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0109] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0110] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0111] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0116] Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field within the technical scope disclosed by the present invention, who makes equivalent replacement or change according to the technical scheme and inventive concept of the present invention, should be covered by the protection scope of the present invention.

Claims

1. A network optimization method for a power line carrier communication system, characterized in that: Step 1: Perform intelligent communication area division and impedance matching in the industrial automation network, and dynamically adjust the industrial automation network; Step 2: Optimize and reconfigure proxy sites in the industrial automation network; Step 3: Analyze the hierarchical structure and traffic requirements in the industrial automation network to develop a load balancing strategy.

2. A method for optimizing a power line carrier communication system network according to claim 1, characterized in that: In the step 1, the process of dividing the intelligent communication area in the industrial automation network includes: Set N device nodes in the industrial automation network, and device node i contains the device node position coordinates NC i =(x i ,y i ) and the communication radius r i ; Where N represents the number of device nodes; A clustering algorithm is used to divide the communication area, and N device nodes are divided into K communication areas, so that the communication quality of nodes in each communication area is optimized; wherein, the clustering algorithm formula is: Where F(R) represents the communication area partition function value; R represents the communication area partition set, and R = {R1, R2, ..., R K }; R k represents the kth divided communication area; K represents the number of communication areas obtained after the communication area division is completed; k represents the communication area index obtained after the communication area division is completed; D(NC i ,NC j ) represents the distance between device node i and device node j; r i 、r j Respectively represent the communication radius of the i-th and j-th device nodes; i and j represent the device node index; NC j Indicates the location coordinates of device node j.

3. A method for optimizing a power line carrier communication system network according to claim 2, characterized in that: In the step 1, the process of performing impedance matching in the industrial automation network and dynamically adjusting the industrial automation network includes: In the same communication area, the channel impedance value between device nodes is marked as Z ij ; By adjusting the channel impedance value, the channel attenuation can be reduced to a minimum. The formula for adjusting the channel impedance value is: Where L(Z) represents the minimized channel attenuation function; Z represents the impedance matrix; Z' ij Indicates the target impedance value; Get the industrial automation network link communication quality matrix Q = (Q ij ), where Q ij Indicates the communication quality between device node i and device node j; Regularly update the industrial automation network link communication quality matrix Q, according to the new industrial automation network link communication quality matrix Q (τ+1) Dynamically adjust the industrial automation network; Q (τ+1) =Q (τ) +ΔQ, where τ represents the number of updates; Q (τ+1) represents the updated industrial automation network link communication quality matrix; Q (τ) represents the current industrial automation network link communication quality matrix; ΔQ represents the increment of the industrial automation network link communication quality matrix.

4. The method for optimizing a power line carrier communication system network according to claim 1, characterized in that: In step 2, the process of optimizing and reconfiguring the proxy site in the industrial automation network includes: Statistically obtain the proxy site set A of the industrial automation network, the lth proxy site A l There is a proxy site location vector PC l and communication capabilities l ; Where l represents the proxy site index; Obtain the average delay Tavg, proxy site load LD, and network link information NL for all data packet transmissions in the industrial automation network; The overall performance of the proxy site is quantitatively analyzed using the performance evaluation function g(A); the formula of the performance evaluation function g(A) is: In the formula, It represents the inverse indicator of the communication efficiency of the industrial automation network, where Tavg represents the average delay of all data packets transmitted in the industrial automation network; represents the proxy site load balancing index; σLD represents the standard deviation of the proxy site load; LD(max) represents the maximum value of the proxy site load; represents the redundancy index of the industrial automation network; μ1(NL) represents the number of redundant links or nodes in the network link information NL; μ2(NL) represents the number of total links or nodes in the network link information NL; α, β, and γ are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively; Combined with the performance evaluation function g(A), the optimal proxy site is selected from the candidate proxy site set A' for reconfiguration: In the formula, A 0 represents the optimal proxy site set, that is, the proxy site set selected from the candidate site set A' that makes the performance evaluation function g(A) reach the maximum value; argmax is an operator used to find the proxy site set A that makes the performance evaluation function g(A) reach the maximum value; According to the reconfiguration results of the proxy site set, the optimal proxy site location vector is determined and communication capabilities 5. The method for optimizing a power line carrier communication system network according to claim 1, characterized in that: In step 3, the process of analyzing the hierarchical structure in the industrial automation network includes: Use the hierarchical analysis method to analyze the hierarchical structure in the industrial automation network: establish a hierarchical structure for the industrial automation network and divide the industrial automation network into H levels; each level contains n h devices, h represents the index of the hierarchical structure, h = 1, 2, ..., H, H represents the total number of divided levels; For level h, establish an n h ×n h Judgment Matrix B h , used to compare the communication requirements between device nodes in level h; where the judgment matrix B h Elements in Indicates the degree of communication demand of device u relative to device v, and devices u and v correspond to judgment matrix B h The u-row elements and v-row elements of ; For the judgment matrix B h Normalize and get the weight vector w h ; Among them, the normalization formula is: In the formula, represents the weight of device u in level h; Represents the judgment matrix B h The sum of the elements in the uth row and the sum of the elements in the vth row; Calculate the judgment matrix B h The consistency index CI h : In the formula, Represents the judgment matrix B h The maximum eigenvalue, consistency index CI h Used to measure the consistency of the judgment matrix, n h Represents the judgment matrix B h The size of the judgment matrix B h There are h Row and n h List; Get the random consistency index RI h , used to calculate the consistency ratio CR h ; Based on the judgment matrix B h The consistency index CI h and random consistency index RI h To perform a consistency check: In the formula, CR h Indicates the consistency ratio. If CR h <0.1, then it is considered that the judgment matrix B h Be consistent; According to the weight vector w h And the judgment matrix B h , determine the communication path corresponding to the device at level h.

6. A method for optimizing a power line carrier communication system network according to claim 5, characterized in that: In step 3, the process of analyzing the traffic demand in the industrial automation network and formulating a load balancing strategy includes: The communication paths between device nodes in the industrial automation network are regarded as edges, and an industrial automation network flow graph G = (V, E) is constructed, where V represents the device node set and E represents the edge set; G = (V, E) is a directed graph, that is, a graph composed of the device node set V and the edge set E, which represents all device nodes and communication paths in the industrial automation network; For any two device nodes i and j, establish the traffic balance equation: In the formula, f ij represents the actual traffic from device node i to device node j, that is, the actual traffic of edge E(i,j), f ji Represents the actual traffic from device node j to device node i, that is, the actual traffic of edge E(j,i).

Images