Fault positioning system between transmission networks

Abstract

The invention discloses a fault positioning system between transmission networks, which dynamically considers the mutual relationship and communication state between nodes through a multi-layer node interaction model, and can judge the fault through interaction data between the nodes not only based on a traditional threshold method, so that the fault positioning accuracy is improved. Therefore, the problems of false alarm and missing alarm in a fixed threshold value method are avoided. Through fine analysis of the model, the fault node can be detected more accurately, especially in a complex and dynamic network environment. Through combination of a multi-layer node interaction model and ant colony algorithm optimization, the position of a fault node can be more accurately positioned. The problem that a traditional graph theory method is low in precision is solved, however, according to the method, the fault positioning result is continuously iteratively optimized by simulating the behavior that ants find a path, the positioning error is reduced, and the positioning precision is improved. Experimental results show that the positioning error of the scheme is greatly lower than that of the existing technical scheme, and the specific position of the fault node can be accurately identified.

Description

Technical Field

[0001] The present invention belongs to the field of fault location, and specifically relates to a fault location system between transmission networks. Background Art

[0002] With the rapid development and wide application of wireless communication networks, the network scale has been continuously expanding, and the types and functions of nodes have also become increasingly diverse. In such a complex network environment, how to accurately and quickly detect and locate fault nodes has become an important issue for ensuring the stable operation of the network and improving the user experience. Most of the existing fault detection and location technologies rely on traditional fixed-threshold judgment methods or simple graph theory analysis models. Although these methods can identify fault nodes to a certain extent, they have the following problems: Traditional fault detection methods usually make judgments based on fixed thresholds such as signal strength and data traffic. However, with the complication of the wireless communication environment, the node state is affected by various factors, and fixed thresholds may not be able to accurately adapt to all network environments, easily leading to false alarms or missed alarms.

[0003] Existing fault location methods mainly rely on simple graph theory models. Although they can analyze through the connection relationships between nodes, this method usually fails to consider the complex interactions and dynamic changes between nodes, resulting in low location accuracy and difficulty in accurately determining the location of fault nodes.

[0004] In view of this, the present invention is specifically proposed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a fault location system between transmission networks, solving the problems raised in the above background art.

[0006] To solve the above technical problems, the basic concept of the technical solution adopted by the present invention is: A fault location system between transmission networks, comprising: A data acquisition module, which is used to collect the status information of each node in the wireless communication network in real time, and the collected data is transmitted to the fault detection module in real time through sensors or wireless network management devices; A fault detection module, which is used to analyze and detect whether there is a fault according to the collected network status information; A fault location module, which is used to accurately locate the fault node through a multi-layer node interaction model according to the output of the fault detection module; An optimization algorithm module, which is used to optimize the fault location result, correct the location result based on the ant colony algorithm, and the ant colony algorithm gradually converges to the optimal fault location solution by simulating the behavior of ants searching for paths and using the accumulation and update of pheromones.

[0007] Optionally, the steps for real-time collecting the status information of each node in the wireless communication network are as follows: Through sensors or wireless network management devices, the status information of each node in the wireless communication network is collected in real time. The status information of the node includes, but is not limited to, network performance parameters such as signal strength, data traffic, node connection quality, node load, latency, packet loss rate, etc.; The collected node status information is transmitted to the fault detection module, and the data is transmitted through wireless or wired communication channels; The collected node status information is preliminarily preprocessed, and operations such as data cleaning, denoising, and format conversion are performed to ensure the accuracy and reliability of the data.

[0008] Optionally, the transmission method can adopt TCP / IP protocol, UDP protocol, MQTT protocol.

[0009] Optionally, the steps for calculating the difference degree between nodes and determining whether there is a fault in the node by analyzing the status information of each node in the network are as follows: Calculate the status difference degree between each node , and the status difference degree is calculated as follows: , where and are the status information of node and node respectively, and represents the difference degree of the status between nodes; Compare the calculated difference degree with a preset threshold. When exceeds the preset threshold, it is determined that node i is a faulty node; If a faulty node is detected, output the possible location and fault type of the faulty node for use in subsequent fault location or repair steps.

[0010] Optionally, when constructing a multi-layer node interaction model, first, according to the role or location of the nodes in the network, the nodes are divided into multiple functional levels, and the nodes within each level have similar characteristics or functions. Specifically, the nodes can be divided into the following categories: Communication layer: including wireless base stations, routers, switches, etc., for data forwarding and transmission; Network layer: including access points, gateways, etc., connecting different networks; Application layer: including user devices, terminal devices, etc., directly interacting with users.

[0011] ​Within each layer, the interaction relationships between nodes are defined by the characteristics of the nodes. The interaction relationships can be represented by the edges of a graph, and the weights of the edges reflect the communication quality, transmission speed, and load between nodes. After defining the nodes and their interaction relationships in each layer, connections between layers are established to represent the signal transmission and data exchange relationships between different layers.

[0012] Optionally, after constructing the multi-layer node interaction model, specific state characteristics are assigned to each node. These characteristics include but are not limited to signal strength, data traffic, node load, communication delay, etc. The expression is: where, is the state characteristic of node , is the signal strength, is the transmission rate of the node, is the load, is the communication delay.

[0013] Optionally, through the multi-layer node interaction model, the steps for accurately locating a faulty node according to the output of the fault detection module are as follows: According to the output result of the fault detection module, obtain the location of the detected faulty node and the status information of its adjacent nodes; Based on the multi-layer node interaction model, input the status of the faulty node and the status of its adjacent nodes into the model, model through the interaction relationships between nodes, and calculate the influence of each node on the fault location. The expression is: , where, represents the total influence of the faulty node, is the state characteristic of each node, is the fault probability function of the node, and N is the total number of all nodes in the network; Through the calculation of the interaction relationships and state characteristics of the network nodes, accurately locate the position of the faulty node and output the location result for subsequent operations.

[0014] Optionally, optimize the fault location result and correct the location result based on the ant colony algorithm. The steps for the ant colony algorithm to gradually converge to the optimal fault location solution by simulating the behavior of ants searching for paths and using the accumulation and update of pheromones are as follows: Initialize the ant colony algorithm, set the number of ants, the maximum number of iterations, the initial value of pheromones, and the heuristic function. Each ant represents a potential fault location solution, and its task is to gradually update and optimize the fault node location result by simulating the behavior of ants searching for paths; In the network topology diagram, the positions of each node are set, and the weights of the edges are set according to the distances and interaction relationships between the nodes. Ants explore possible fault location solutions by choosing different paths. The probability of path selection is jointly determined by the pheromone concentration and the heuristic function, and its expression is: , where is the selection probability of the ant from node to node . is the current pheromone concentration of edge . is the heuristic function (node load), and are parameters that control the weights of the pheromone and the heuristic function; According to the path selection situation of each ant, calculate the fitness or quality of the path. The fitness function is related to the accuracy of the fault location result. The higher the path quality, the larger the fitness value, and its expression is: , where is the total distance (or cost) of the path selected by the ant, is a constant to avoid division-by-zero errors; Update the pheromone. Update the pheromone according to pheromone evaporation and the fitness value of the ant path, and its expression is: , where is the pheromone evaporation factor, is the current time step of edge 's pheromone concentration, is the pheromone left by the ant on path , usually proportional to the fitness of the path; By repeatedly iterating to update the pheromone and path selection, gradually converge to the optimal fault location solution. After multiple iterations, the pheromone will gradually concentrate on the optimal path, thereby optimizing the fault location result.

[0015] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time: 1. Through the multi-layer node interaction model, dynamically consider the mutual relationships and communication states between nodes. Not only based on the traditional threshold method, but also judge faults through the interaction data between nodes, thus avoiding the false alarm and missed alarm problems existing in the fixed threshold method. Through the refined analysis of the model, the present invention can more accurately detect fault nodes, especially in complex and dynamic network environments.

[0016] 2. By combining a multi-layer node interaction model and ant colony algorithm optimization, the present invention can more accurately locate the position of a faulty node. Traditional graph theory methods have problems with low accuracy. However, the present invention simulates the behavior of ants searching for paths, continuously iteratively optimizing the fault location result, reducing the location error, and improving the location accuracy. Experimental results show that the location error of the present solution is significantly lower than that of existing technical solutions, and it can accurately identify the specific location of the faulty node.

[0017] 3. When the ant colony algorithm adopted by the present invention is used for fault location, it can converge to the optimal solution within a certain calculation time, reducing the burden brought by complex calculations and large-scale data processing. Compared with existing graph theory-based fault location methods, the present invention achieves a good balance in terms of location accuracy and real-time performance, and is applicable to real-time fault location in large-scale wireless communication networks.

[0018] The following further describes in detail the specific implementation manners of the present invention with reference to the accompanying drawings. Description of the Drawings

[0019] The following drawings in the description are only some embodiments. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings: Figure 1 It is a flowchart of a fault location system between transmission networks.

[0020] It should be noted that these drawings and textual descriptions are not intended to limit the scope of the concept of the present invention in any way, but to illustrate the concept of the present invention to those skilled in the art by referring to specific embodiments. Specific Implementation Manner

[0021] Now, the present invention will be further described in detail with reference to the accompanying drawings.

[0022] Please refer to Figure 1 As shown, in this embodiment, a fault location system between transmission networks is provided, including a data acquisition module. The data acquisition module is used to collect the status information of each node in the wireless communication network in real time, including signal strength, data traffic, node connection quality, and other network performance parameters. The collected data is transmitted to the fault detection module in real time through sensors or wireless network management devices; A fault detection module, which is used to analyze and detect whether there is a fault according to the collected network status information. If a network anomaly is detected, it determines whether it is a faulty node and outputs the possible location and fault type of the faulty node; A fault location module, which is used to accurately locate the faulty node through a multi-layer node interaction model according to the output of the fault detection module; Optimization algorithm module, used to optimize the fault location results, correct the location results based on the ant colony algorithm, and the ant colony algorithm gradually converges to the optimal fault location solution by simulating the behavior of ants searching for paths and using the accumulation and update of pheromones.

[0023] In this embodiment, The state information of each node in the wireless communication network is collected in real time through sensors or wireless network management devices, and the state information of the nodes includes, but is not limited to, network performance parameters such as signal strength, data traffic, node connection quality, node load, delay, and packet loss rate; The collected node state information is transmitted to the fault detection module, and the data is transmitted through wireless or wired communication channels; The collected node state information is preliminarily preprocessed, and operations such as data cleaning, denoising, and format conversion are performed to ensure the accuracy and reliability of the data. The data processing can be carried out through a data processing module to ensure that the data transmitted to the fault detection module meets the requirements of real-time and accuracy.

[0024] In this embodiment, the transmission method can adopt TCP / IP protocol, UDP protocol, or MQTT protocol.

[0025] In this embodiment, the steps of calculating the difference degree between nodes and judging whether a node has a fault by analyzing the state information of each node in the network are as follows: Calculate the state difference degree between each node , and the state difference degree The calculation formula is as follows: , where and are the state information of node and node respectively, and represents the difference degree between the states of nodes; Compare the calculated difference degree with a preset threshold. When exceeds the preset threshold, it is determined that node i is a faulty node; If a faulty node is detected, output the possible location and fault type of the faulty node for use in subsequent fault location or repair steps.

[0026] In this embodiment, when constructing a multi-layer node interaction model, first, according to the role or location of the nodes in the network, the nodes are divided into multiple functional levels, and the nodes within each level have similar characteristics or functions. Specifically, the nodes can be divided into the following categories: Communication layer: including wireless base stations, routers, switches, etc., used for data forwarding and transmission; Network layer: including access points, gateways, etc., which connect different networks; Application layer: including user devices, terminal devices, etc., which directly interact with users.

[0027] Within each layer, the interaction relationships between nodes are defined through the characteristics of the nodes (such as network topology, communication method, etc.). The interaction relationships can be represented by the edges of a graph, and the weights of the edges reflect the communication quality, transmission speed, and load between nodes; After defining the nodes and their interaction relationships in each layer, connections between layers are established to represent the signal transmission and data exchange relationships between different layers. These relationships can be represented by a multi-layer network topology graph, and the distinct layers of the topology graph help to identify the dependencies between layers.

[0028] In this embodiment, after constructing the multi-layer node interaction model, specific state characteristics are assigned to each node. These characteristics include but are not limited to signal strength, data traffic, node load, communication delay, etc., and their expression is: Among them, is the state characteristic of node , is the signal strength, is the transmission rate of the node, is the load, is the communication delay.

[0029] In this embodiment, through the multi-layer node interaction model, the steps for accurately locating a faulty node according to the output of the fault detection module are as follows: According to the output result of the fault detection module, obtain the location of the detected faulty node and the status information of its adjacent nodes; Based on the multi-layer node interaction model, input the status of the faulty node and the status of its adjacent nodes into the model, model through the interaction relationships between nodes, and calculate the influence of each node on the fault location. Its expression is: , where represents the total influence of the faulty node, is the state characteristic of each node, is the fault probability function of the node, and N is the total number of all nodes in the network; this model calculates the location of the faulty node according to the state characteristics of each node and its contribution to the fault.

[0030] By calculating the interaction relationships and state characteristics of the network nodes, accurately locate the location of the faulty node and output the location result for subsequent operations.

[0031] In this embodiment, the fault location result is optimized, and the location result is corrected based on the ant colony algorithm. The steps of the ant colony algorithm to gradually converge to the optimal fault location solution by simulating the behavior of ants searching for paths and using the accumulation and update of pheromones are as follows: Initialize the ant colony algorithm, set the number of ants, the maximum number of iterations, the initial value of pheromone, and the heuristic function. Each ant represents a potential fault location solution, and its task is to gradually update and optimize the location result of the fault node by simulating the behavior of ants searching for paths; In the network topology graph, set the position of each node, and set the weight of the edge according to the distance and interaction relationship between the nodes. The ants explore possible fault location solutions by choosing different paths, and the probability of path selection is jointly determined by the pheromone concentration and the heuristic function. Its expression is: , where is the selection probability of the ant from node to node , is the current pheromone concentration of edge , is the heuristic function (node load), and are parameters that control the weights of pheromone and heuristic function; According to the path selection situation of each ant, calculate the fitness or quality of the path. The fitness function is related to the accuracy of the fault location result. The higher the path quality, the larger the fitness value. Its expression is: , where is the total distance (or cost) of the path selected by the ant, is a constant to avoid division-by-zero error; Update the pheromone, and update the pheromone according to the pheromone evaporation and the fitness value of the ant path. Its expression is: , where is the pheromone evaporation factor, is the current time step of edge 's pheromone concentration, is the pheromone left by the ant on path , usually proportional to the fitness of the path; By repeatedly iterating to update the pheromone and path selection, gradually converge to the optimal fault location solution. After multiple iterations, the pheromone will gradually concentrate on the optimal path, thereby optimizing the fault location result.

[0032] Existing technical solution 1: Fault detection based on traditional threshold detection and rule engine In traditional fault detection systems, fixed thresholds are usually used to detect faults in wireless communication networks. For example, when the signal strength of a node is lower than a certain fixed value or the data traffic drops below a certain threshold, the node is determined to be a faulty node. This method mainly relies on preset thresholds and rules, lacks dynamic learning and intelligent adaptability to the network state, and cannot effectively cope with changes and sudden faults in complex network environments.

[0033] Existing technical solution 2: Fault location based on a simple graph theory model Another existing technical solution is to analyze the connection relationships between nodes through a graph theory model to detect faults in network nodes. By constructing a graphical model of nodes, analyzing the connection situation of each node, and combining signal strength and transmission rate to judge faults. This method can identify faults to a certain extent, but due to the failure to effectively consider the complex interaction relationships between nodes, the location accuracy is low, and the computational complexity is high in large-scale network environments.

[0034] 2. Equivalent conditions for comparative experiments To ensure a fair comparison between the present invention and existing technical solutions, the following are the equivalent conditions for the comparative experiments: Experimental environment: All solutions are experimentally tested in the same wireless communication network environment. Assume that the network consists of 100 nodes, 30% of which are faulty nodes, randomly distributed, and the fault types are signal strength drop, abnormal data traffic, and poor connection quality.

[0035] Network parameters: Node signal strength range: 0 - 100 (a signal strength lower than 30 is considered a fault).

[0036] Node data traffic: The data traffic range of each node is 0 - 1000 kbps.

[0037] Connection quality: Represented by the packet loss rate, a connection quality lower than 90% is considered a fault.

[0038] Experimental method: In existing technical solution 1, fixed signal strength and data traffic thresholds are used to detect node faults.

[0039] In existing technical solution 2, by constructing a graph theory model, calculating the connection degree and communication quality between nodes to detect faults.

[0040] The present invention uses a multi-layer node interaction model and ant colony algorithm optimization to locate faulty nodes, and performs location optimization through dynamic data and pheromone update.

[0041] Evaluation metrics: Detection accuracy rate: The proportion of successfully detected faulty nodes.

[0042] Location accuracy: The positioning error of the faulty node, which is calculated as the difference between the actual position and the true position of the detected faulty node.

[0043] Computational complexity: The time required to complete fault detection and location.

[0044] 3. After comparison, the beneficial effects of the present invention are shown through data or charts. The following is the display of the comparison experiment results: (1) Comparison of detection accuracy

[0045] It can be seen from the comparison that the present invention has a significantly improved fault detection accuracy compared with the existing technical solutions 1 and 2. This is because the present invention adopts a multi-layer node interaction model, which can more accurately judge the faulty node according to the dynamic interaction relationship between nodes, rather than simply relying on fixed thresholds or simple graph theory analysis.

[0046] (2) Comparison of location accuracy

[0047] The present invention effectively reduces the positioning error through an optimized algorithm (ant colony algorithm) and a multi-layer node interaction model, and the location accuracy is significantly higher than that of the existing technical solutions 1 and 2. The ant colony algorithm can optimize the positioning result in each iteration and finally converge to the optimal solution, thereby improving the location accuracy.

[0048] (3) Comparison of computational complexity

[0049] Although the computational complexity of the present invention is higher than that of the existing technical solution 1 (due to the adoption of a complex interaction model and the ant colony algorithm), compared with the existing technical solution 2, the computational time of the present invention is much lower. This shows that the present invention can still maintain a relatively reasonable computational efficiency while achieving high-precision fault location.

[0050] The present invention is not limited to the above embodiments. Anyone should know that structural changes made under the inspiration of the present invention, as long as they have the same or similar technical solutions as the present invention, shall fall within the protection scope of the present invention. The technologies, shapes, and structures not described in detail in the present invention are all well-known technologies.

Claims

1. A fault location system between transmission networks, characterized in that, Including: A data acquisition module, which is used to collect the status information of each node in the wireless communication network in real time, and the collected data is transmitted to the fault detection module in real time through sensors or wireless network management devices; A fault detection module, which is used to analyze and detect whether there is a fault according to the collected network status information; A fault location module, which is used to accurately locate the fault node according to the output of the fault detection module through a multi-layer node interaction model; An optimization algorithm module, which is used to optimize the fault location result and correct the location result based on the ant colony algorithm. The ant colony algorithm simulates the behavior of ants looking for paths, and uses the accumulation and update of pheromones to gradually converge to the optimal fault location solution.

2. The fault location system between transmission networks according to claim 1, wherein The steps for collecting the status information of each node in the wireless communication network in real time are as follows: Through sensors or wireless network management devices, collect the status information of each node in the wireless communication network in real time. The status information of the node includes, but is not limited to, network performance parameters such as signal strength, data traffic, node connection quality, node load, delay, and packet loss rate; Transmit the collected node status information to the fault detection module, and the data is transmitted through wireless or wired communication channels; Perform preliminary preprocessing on the collected node status information, such as data cleaning, denoising, and format conversion, to ensure the accuracy and reliability of the data.

3. A fault location system between transmission networks according to claim 1, characterized in that, The transmission mode can adopt TCP / IP protocol, UDP protocol, MQTT protocol.

4. A fault location system between transmission networks according to claim 1, characterized in that, The steps for calculating the difference degree between nodes and judging whether there is a fault in the node by analyzing the status information of each node in the network are as follows: Calculate the state difference degree between each node , the state difference degree is calculated as follows: , where and are the state information of node and node respectively, and represents the state difference degree between nodes; The calculated difference degree is compared with a preset threshold. When it exceeds the preset threshold, node i is determined to be a faulty node; If a fault node is detected, output the possible location and fault type of the fault node for use in subsequent fault location or repair steps.

5. A fault location system between transmission networks according to claim 1, characterized in that, When constructing a multi-layer node interaction model, first, divide the nodes into multiple functional levels according to the role or location of the nodes in the network. The nodes within each level have similar characteristics or functions. Specifically, the nodes can be divided into the following categories: Communication layer: including wireless base stations, routers, switches, etc., for data forwarding and transmission; Network layer: including access points, gateways, etc., connecting different networks; Application layer: including user devices, terminal devices, etc., directly interacting with users; Within each level, define the interaction relationship between nodes through the characteristics of the nodes. The interaction relationship can be represented by the edges of a graph, and the weight of the edge reflects the communication quality, transmission speed, and load between nodes; After defining the nodes and their interaction relationships at each level, establish the connection between levels to represent the signal transmission and data exchange relationships between different levels.

6. The fault location system between transmission networks according to claim 1, wherein After constructing a multi-layer node interaction model, specific state features are assigned to each node, and these features include but are not limited to signal strength, data traffic, node load, communication delay, etc., and their expressions are as follows: Among them, is the state feature of the node , is the signal strength, is the transmission rate of the node, is the load, is the communication delay.

7. A fault location system between transmission networks according to claim 1, characterized in that, The steps for accurately locating the fault node according to the output of the fault detection module through a multi-layer node interaction model are as follows: According to the output result of the fault detection module, obtain the location of the detected fault node and the status information of its adjacent nodes; Based on the multi - layer node interaction model, the state of the faulty node and the states of its adjacent nodes are input into the model. Through the interaction relationship between nodes, modeling is carried out to calculate the influence of each node on the fault location, and its expression is: , where represents the total influence of the faulty node, is the state feature of each node, is the fault probability function of the node, and N is the total number of all nodes in the network; Through the calculation of the interaction relationship and status characteristics of the network nodes, accurately locate the location of the fault node and output the location result for subsequent operations.

8. A fault location system between transmission networks according to claim 1, characterized in that, Optimize the fault location result, and correct the location result based on the ant colony algorithm. The steps for the ant colony algorithm to gradually converge to the optimal fault location solution by simulating the behavior of ants searching for paths and using the accumulation and update of pheromones are as follows: Initialize the ant colony algorithm, and set the number of ants, the maximum number of iterations, the initial value of pheromones, and the heuristic function. Each ant represents a potential fault location solution, and its task is to gradually update and optimize the location result of the fault node by simulating the behavior of ants searching for paths; In the network topology diagram, the positions of each node are set, and the weights of the edges are set according to the distances and interaction relationships between the nodes. Ants explore possible fault location solutions by choosing different paths. The probability of path selection is jointly determined by the pheromone concentration and the heuristic function, and its expression is: , where is the selection probability of the ant from node to node , is the current pheromone concentration of edge , is the heuristic function (node load), and are parameters that control the weights of the pheromone and the heuristic function; According to the path selection of each ant, calculate the fitness or quality of the path. The fitness function is related to the accuracy of the fault location result. The higher the path quality, the larger the fitness value, and its expression is: , where is the total distance (or cost) of the path selected by the ant, is a constant to avoid division-by-zero errors; Update pheromone, and update the pheromone according to the pheromone evaporation and the fitness value of the ant path. Its expression is: , where is the pheromone evaporation factor, is the current time step of the edge pheromone concentration, is the pheromone left by the ant on the path , usually proportional to the fitness of the path; Gradually converge to the optimal fault location solution by repeatedly updating pheromones and path selection. After multiple iterations, the pheromones will gradually concentrate on the optimal path, thereby optimizing the fault location result.

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