A method for locating faults in an IPTV network

Abstract

The embodiment of the present disclosure provides an IPTV network fault positioning method, and belongs to the technical field of electric communication, and specifically comprises the following steps: defining a target network as an undirected connected graph, defining a detection range of a group of probes at a position of a terminal node, defining a load of each node in the target network, and defining a benefit function of each node; iteratively selecting a position with the maximum benefit in each node as a starting probe to form a node position set; according to a measurement result of each starting probe in the node position set, marking a node type on a detection path to generate a detection result set; generating a new detection path set for a suspected node set and selecting a suitable detection path until the detection path set is empty or all suspected nodes are determined, and adding a newly detected fault node to a fault node set to generate a fault positioning result. Through the scheme of the present disclosure, the detection coverage, the positioning success rate of node faults and the fault positioning cost are improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of electrical communication technology, and more particularly to an IPTV network fault locating method. Background Art

[0002] Currently, the complete IPTV fault detection, location, and repair process involves repeatedly collecting information, attempting to locate the fault, and troubleshooting until the fault is completely repaired. However, due to the complexity of IPTV networks, the causes of faults are diverse, resulting in low network fault location accuracy and efficiency. In addition, the disorganized and redundant nature of the collected information makes it difficult for operations and maintenance personnel to locate the fault within this chaotic information. Even if a fault diagnosis conclusion is drawn based on some fault information, it can only be considered as probabilistic information about the fault, and subsequent information collection must be gradually eliminated before further diagnostic decisions and adjustments can be made. Currently, IPTV network fault detection and location mainly face the following problems:

[0003] (1) Traditional manual fault location methods have problems such as high costs. The process from the content provider's server to the IPTV set-top box requires multiple devices and lines. If any of these devices or lines fails, service stability will be affected. Manual fault location analysis relies on personal experience. Operations and maintenance personnel typically conduct on-site fault detection, coordinate data acquisition from different network nodes and departments, and then analyze the data and repeatedly test the cause of the fault. This consumes a lot of human resources and has very high coordination and communication costs.

[0004] (2) Traditional fault location methods based on artificial intelligence, graph theory, and large data sets have problems such as high location cost, low location success rate, and low detection coverage. There are tens of thousands of devices in the network, and too many probes are started at the same time to perform network performance testing, resulting in the system storing a large amount of network device status information, log data, alarm information, etc.; excessive data volume will lead to waste of storage space and increase network load, which may reduce the accuracy of fault location; a large amount of meaningless feature data will cause data redundancy, and data processing is also very expensive, which increases the cost of fault location; in addition, the dynamic and scalable nature of the network will also lead to uncertainty in model performance, and these models have problems such as inconsistent parameters and difficulty in obtaining and updating.

[0005] It can be seen that there is an urgent need for an IPTV network fault location method that can improve detection coverage and success rate and reduce fault location costs. Summary of the Invention

[0006] In view of this, an embodiment of the present disclosure provides an IPTV network fault location method, which at least partially solves the problems of low location success rate and high location cost in the prior art.

[0007] The present disclosure provides an IPTV network fault location method, including:

[0008] Step 1: define the target network as an undirected connected graph, define the detection range of a set of probes at the terminal node location, define the load of each node in the target network, and define the benefit function of each node;

[0009] Step 2: Based on the undirected connectivity graph, detection range, load of each node, and benefit function, iteratively select the location with the maximum benefit in each node as the starting probe to form a node location set;

[0010] Step 3: Based on the end-to-end measurement results of each startup probe in the node location set, the node types on the detection path are marked to generate a detection result set, where the detection result set includes a normal node set, a faulty node set, a suspected node set, and an unknown node set;

[0011] Step 4: Generate a new detection path set for the suspected node set and select a suitable detection path until the detection path set is empty or all suspected nodes are determined and the newly detected faulty node is added to the faulty node set to generate a fault location result.

[0012] According to a specific implementation of the embodiment of the present disclosure, step 1 specifically includes:

[0013] Step 1.1: Define an undirected connected graph G(L, V, E) based on the topology of the target network, where L represents the set of terminal leaf nodes at the bottom layer, V represents the set of intermediate nodes in the graph excluding the terminal nodes, and E represents the set of paths between nodes in two adjacent layers.

[0014] Step 1.2: Define the detection path DP of the probe at the terminal node i i , and accordingly define the detection path of the probe as the sum of the nodes passed by the path and the isolated nodes associated with it, and then take the union of all detection ranges of a group of probes as the detection range DR of a group of probes;

[0015] Step 1.3: Take node i as the root node and take the ratio of the number of leaf nodes it contains to the total number of network nodes N as its load Wi;

[0016] Step 1.4, define the profit function of terminal node i as

[0017]

[0018] Among them, DP i Indicates the detection path of the probe at the location of node i, IN j represents an isolated node on the detection path, DR represents the detection range of a group of probes, and the denominator represents the load sum of the detection path.

[0019] According to a specific implementation manner of the embodiment of the present disclosure, the step 2 specifically comprises:

[0020] Step 2.1, according to the network topology structure G, all intermediate nodes are traversed, and the intermediate node load W is calculated i .

[0021] Step 2.2, all terminal leaf nodes are traversed, and the detection path DP of each candidate probe position is calculated i , according to the detection path, the isolated node IN on the detection path is calculated j and the detection path load sum, the detection range DR of the probe is obtained, if there are multiple paths from the probe to the root node, the path with the minimum node load sum is taken.

[0022] Step 2.3, the algorithm is looped for a preset number of times, and each time the position with the highest current benefit is selected to start the probe, that is, the benefit value V of each leaf node is iteratively calculated i and the node with the maximum benefit is selected, after each selection of the position of the probe is added to the starting probe position set PS, it is deleted from the silent probe set, and then the benefit value of the remaining terminal node is recalculated, until the number of selected probes is the same as the preset number, and the node position set is obtained.

[0023] According to a specific implementation manner of the embodiment of the present disclosure, the step 3 specifically comprises:

[0024] The server end activates the starting probe at the position according to the node position set, simultaneously issues a distributed active detection task, the activated starting probe actively connects the server for network performance measurement and media quality monitoring, and feeds back data to the server end, the server end analyzes and processes the information fed back by the probe, and generates a fault detection result in combination with the network topology structure;

[0025] According to the fault detection result, the type of each node is judged;

[0026] If the fault detection result is normal, the node on the detection path is added to the normal node set;

[0027] If the fault detection result is abnormal, the node on the detection path is added to the suspected node set;

[0028] If there is only one suspected node, it is determined as a fault node and added to the fault node set;

[0029] If the suspected node cannot be reached from any normal node, it is determined as an unknown node and added to the unknown node set;

[0030] If a node is not passed by any detection path, it is determined to be an unknown node and added to the unknown node set.

[0031] According to a specific implementation of the embodiment of the present disclosure, step 4 specifically includes:

[0032] When the current suspected node set is not empty, traverse the suspected node set, calculate the detection path from the root node to each suspected node, generate a set of candidate detection paths, and calculate the weight w(p) of each candidate path;

[0033] Select the candidate path with the highest weight for detection. If the detection is successful, the suspected node will be added to the normal node.

[0034] If the detection fails and the suspected node on the path is 1, the suspected node is judged as a faulty node. If the suspected node on the path is not 1, the end node of the path is changed to an unknown node and marked;

[0035] If the parent node of the marked node is a normal node, the marked node is changed to a faulty node;

[0036] After a preset number of faulty nodes are determined, they are added to the faulty node set, and candidate detection paths are regenerated and determined for the remaining suspected nodes. The updated status of each node is output to form the fault location result.

[0037] According to a specific implementation of the embodiment of the present disclosure, the calculation formula of the weight value is:

[0038]

[0039] Where s(p) represents the number of suspected nodes on the detection path p, E(p) represents the set of all links on the path p, and n(l) is the number of times the selected detection path passes through the path.

[0040] The IPTV network fault location solution in the embodiment of the present disclosure includes the following steps: Step 1: defining a target network as an undirected connected graph, defining the detection range of a set of probes at the location of the terminal node, defining the load of each node in the target network, and defining a benefit function for each node; Step 2: iteratively selecting the location of each node with the maximum benefit as a starting probe based on the undirected connected graph, the detection range, the load of each node, and the benefit function, to form a node location set; Step 3: marking the node type on the detection path based on the end-to-end measurement results of each starting probe in the node location set, and generating a detection result set, wherein the detection result set includes a normal node set, a faulty node set, a suspected node set, and an unknown node set; Step 4: generating a new detection path set for the suspected node set and selecting an appropriate detection path until the detection path set is empty or all suspected nodes have been determined and the newly detected faulty node is added to the faulty node set, thereby generating a fault location result.

[0041] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, the probe selection method is started to use the probe detection range, combined with the network topology node weight, to calculate the benefit of each probe node, and the terminal node with the maximum benefit is iteratively selected to start the deployed probe software and issue a measurement task. According to the probe fault detection results, the node types on the detection path are first classified, and then the optimal detection path is iteratively selected through the adaptive fault node judgment method to perform fault judgment on the suspected node. This fault location method can improve the success rate of node fault location and reduce the fault location cost while ensuring the detection coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 A flowchart of an IPTV network fault location method provided by an embodiment of the present disclosure;

[0044] Figure 2 A schematic diagram of a probe detection range of a node 12 provided in an embodiment of the present disclosure;

[0045] Figure 3 A detection range diagram after starting a soft probe at the locations of nodes 7 and 12 provided in an embodiment of the present disclosure;

[0046] Figure 4 A node type diagram marked according to fault detection results provided by an embodiment of the present disclosure;

[0047] Figure 5 A suspected node determination graph is provided for the embodiments of the present disclosure. DETAILED DESCRIPTION

[0048] The embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0049] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in the specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all the embodiments. The present disclosure can also be implemented or applied by other different specific embodiments, and various modifications or changes can be made to the details in the specification without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0050] It should be noted that the various aspects of the embodiments described below are within the scope of the appended claims. It should be apparent that the aspects described herein can be embodied in a wide variety of forms and that any specific structure and / or function described herein is merely illustrative. Based on the teachings provided herein one skilled in the art should be able to contemplate these and similar aspects of the present disclosure. For example, the device and / or method can be implemented using any number of aspects of the present disclosure described herein. Additionally, the device and / or method can be implemented using other structures and / or functionalities in addition to or other than those described herein.

[0051] It should also be noted that the drawings provided in the following embodiments are only schematic and are intended to provide the basic understanding of the present disclosure. In the drawings, only the components related to the present disclosure are shown, not the number, shape and size of the components as in actual implementation, and the shape, number and ratio of each component in actual implementation can be changed arbitrarily, and the layout of the components can also be more complex.

[0052] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, one skilled in the art will understand that the described aspects can be practiced without these specific details.

[0053] The embodiments of the present disclosure provide an IP TV network fault positioning method, which can be applied to the IP TV network fault diagnosis process in the telecommunication scenario.

[0054] Referring to Figure 1 , is a flow chart of an IPTV network fault location method provided by an embodiment of the present disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0055] Step 1: define the target network as an undirected connected graph, define the detection range of a set of probes at the terminal node location, define the load of each node in the target network, and define the benefit function of each node;

[0056] Furthermore, the step 1 specifically includes:

[0057] Step 1.1: Define an undirected connected graph G(L, V, E) based on the topology of the target network, where L represents the set of terminal leaf nodes at the bottom layer, V represents the set of intermediate nodes in the graph excluding the terminal nodes, and E represents the set of paths between nodes in two adjacent layers.

[0058] Step 1.2: Define the detection path DP of the probe at the terminal node i i , and accordingly define the detection path of the probe as the sum of the nodes passed by the path and the isolated nodes associated with it, and then take the union of all detection ranges of a group of probes as the detection range DR of a group of probes;

[0059] Step 1.3: Take node i as the root node and take the ratio of the number of leaf nodes it contains to the total number of network nodes N as its load Wi;

[0060] Step 1.4, define the profit function of terminal node i as

[0061]

[0062] Among them, DP i Indicates the detection path of the probe at the location of node i, IN j represents an isolated node on the detection path, DR represents the detection range of a group of probes, and the denominator represents the load sum of the detection path.

[0063] The fault location of this method mainly includes the steps of probe selection, fault detection, node marking and suspected node determination. Given an IPTV hierarchical network topology, that is, the location of network nodes and the connection mode between nodes are known. This method aims to reduce the detection load and improve the detection benefit. It dynamically selects soft probes deployed on the end-user set-top box and issues detection tasks. The soft probe uploads the measurement results to the server. The server uses the collected probe data and related information for analysis and fault detection. It marks different types of nodes in combination with the network topology. Then, it regenerates candidate detection paths for the suspected nodes generated by the detection results, selects appropriate paths to detect suspected nodes, and finally achieves fault location.

[0064] In specific implementations, a layered network is represented by an undirected connected graph G(L, V, E), where L represents the bottom-level terminal leaf nodes, where soft probes are deployed. V represents the set of intermediate nodes in the graph, excluding the terminal nodes, and generally represents intermediate network devices. E represents the set of paths between adjacent nodes, and the root node generally represents the server location of the probe management platform. When probes are deployed at the bottom-level terminal nodes in the network, they can transmit data along the network, detecting the status of nodes and links along the path. They can also perform fault inference on isolated nodes connected to the probed path.

[0065] Define the detection path DPi (Detection Path) of the probe at the location of terminal node i, that is, the transmission path from the node i where the probe is deployed to the root node. It is a set of intermediate nodes passed through the hierarchical network, and includes the node where the probe itself is deployed.

[0066] Define the associated isolated node set IN (Isolated Node) for terminal node i. This is the set of isolated nodes associated with node i through which the detection path DP passes. An associated isolated node is one whose sole parent node is in the detection path DPi and has no child nodes. The detection range of a single probe is defined as the sum of the nodes passed by the probe's detection path and its associated isolated nodes. A set of probes (PS) has a detection range DR (Detection Range), which is the union of all detection ranges of the probes.

[0067]

[0068] To quantify the load on each node in the network, we define the load Wi of node i as the ratio of the number of leaf nodes in the root node to the total number of nodes in the network, N. The numerator represents the sum of the leaf nodes in all subtrees rooted at node j, with node i as the root. That is, if node j is a child of the subtree of node i, Cij = 1; otherwise, Cij = 0.

[0069]

[0070] Define the profit function Vi for terminal node i. This function represents the profit of initiating a probe at that terminal node in the network. It is calculated based on the current number of detectable nodes and the sum of the detection path loads. A higher Vi indicates a greater profit for initiating a probe at that node. The calculation method is as follows: The numerator is the nodes within the detection range of the current node i, excluding nodes already within the detection range of the probe set. The larger the current node's detectable range, the higher the profit. The denominator is the sum of the node loads on the detection path from node i to the root node. A higher sum of the detection path loads indicates a lower profit for initiating a probe at that node.

[0071]

[0072] Step 2: Based on the undirected connectivity graph, detection range, load of each node, and benefit function, iteratively select the location with the maximum benefit in each node as the starting probe to form a node location set;

[0073] Based on the above embodiment, step 2 specifically includes:

[0074] Step 2.1: According to the network topology G, traverse all intermediate nodes and calculate the intermediate node load W i .

[0075] Step 2.2, traverse all terminal leaf nodes and calculate the detection path DP of each candidate probe position i , based on these detection paths, calculate the isolated nodes IN on the detection path j And the detection path load sum is used to obtain the detection range DR of the probe. If there are multiple paths from the probe to the root node, the path with the smallest node load sum is selected.

[0076] Step 2.3: The algorithm loops for a preset number of times, and each time it selects the location with the highest current profit to start the probe. That is, the algorithm iteratively calculates the profit value V of each leaf node. i And select the node with the largest benefit. After each probe position is selected and added to the startup probe position set PS, it is deleted from the silent probe set, and then the benefit value of the remaining terminal nodes is recalculated until the number of selected probes is the same as the preset number, and the node position set is obtained.

[0077] For example, the algorithm input is the hierarchical network topology G, the number of activated probes M, and the algorithm output is the node location set PS where the soft probes to be activated are deployed.

[0078] The algorithm first traverses all intermediate nodes according to the network topology G and calculates the intermediate node load Wi.

[0079] Next, we traverse all terminal leaf nodes and calculate the detection paths DPi for each candidate probe location. Based on these detection paths, we calculate the isolated nodes INj along the detection paths and the detection path load (the denominator of the benefit Vi), thus obtaining the detection range DR of the probe. If there are multiple paths from the probe to the root node, we select the path with the smallest sum of node loads.

[0080] Finally, the algorithm loops N times, and each time the position with the highest current benefit is selected to start the probe. That is, the algorithm iteratively calculates the benefit value Vi of each leaf node and selects the node with the largest benefit. After each probe position is selected and added to the starting probe position set, it needs to be deleted from the silent probe set, and then the benefit value of the remaining terminal nodes is recalculated until the number of selected probes is M, and the node position set is obtained. For example, the detection range of the probe at the location of node 12 is as follows: Figure 2 shown.

[0081] Step 3: Based on the end-to-end measurement results of each startup probe in the node location set, the node types on the detection path are marked to generate a detection result set, where the detection result set includes a normal node set, a faulty node set, a suspected node set, and an unknown node set;

[0082] Furthermore, the step 3 specifically includes:

[0083] The server activates the startup probes at the node locations based on the node location set and simultaneously issues distributed active detection tasks. The activated probes proactively connect to the server to measure network performance and monitor media quality, and then feed the data back to the server. The server analyzes and processes the information fed back by the probes and generates fault detection results based on the network topology.

[0084] Determine the type of each node based on the fault detection results;

[0085] If the fault detection result is normal, the nodes on the detection path are added to the normal node set;

[0086] If the fault detection result is abnormal, the nodes on the detection path are added to the set of suspected nodes;

[0087] If there is only one suspected node, it is determined to be a faulty node and added to the faulty node set;

[0088] If a suspected node cannot be reached from any normal node, it is determined to be an unknown node and added to the unknown node set;

[0089] If a node is not passed by any detection path, it is determined to be an unknown node and added to the unknown node set.

[0090] In the specific implementation, the detection range after starting the soft probe at the location of node 7 and node 12 is as follows Figure 3As shown in the figure, the server can activate soft probes at the locations based on the constructed node location set PS and simultaneously issue distributed active detection tasks. The activated probes actively connect to the server to measure network performance and monitor media quality, and then feed the data back to the server. The server analyzes and processes the information fed back by the probes and, based on the network topology, implements rapid fault detection.

[0091] All nodes in the network are divided into normal node set NNS, fault node set FNS, suspected node set SNS, and unknown node set UNS.

[0092] Based on the fault detection results of the startup probe, if the soft probe fault detection is normal, the nodes on the detection path are added to the normal node set. If the soft probe fault detection is abnormal, the nodes on the detection path are added to the suspected node set. If there is only one suspected node, it can be determined as a faulty node. If a suspected node cannot be reached from any normal node, it is determined to be an unknown node. If a node is not passed by any detection path, it is also determined to be an unknown node. The process of marking the node type according to the fault detection results is as follows: Figure 4 As shown, Figure 4 (a) shows the marking of network topology node types when the probe detection result at the location of node 7 is normal and the probe detection result at the location of node 12 is abnormal; Figure 4 (b) shows the marking of network topology node types when the probe detection result at the location of node 12 is normal and the probe detection result at the location of node 7 is abnormal.

[0093] Step 4: Generate a new detection path set for the suspected node set and select a suitable detection path until the detection path set is empty or all suspected nodes are determined and the newly detected faulty node is added to the faulty node set to generate a fault location result.

[0094] Based on the above embodiment, step 4 specifically includes:

[0095] When the current suspected node set is not empty, traverse the suspected node set, calculate the detection path from the root node to each suspected node, generate a set of candidate detection paths, and calculate the weight w(p) of each candidate path;

[0096] Select the candidate path with the highest weight for detection. If the detection is successful, the suspected node will be added to the normal node.

[0097] If the detection fails and the suspected node on the path is 1, the suspected node is judged as a faulty node. If the suspected node on the path is not 1, the end node of the path is changed to an unknown node and marked;

[0098] If the parent node of the marked node is a normal node, the marked node is changed to a faulty node;

[0099] After a preset number of faulty nodes are determined, they are added to the faulty node set, and candidate detection paths are regenerated and determined for the remaining suspected nodes. The updated status of each node is output to form the fault location result.

[0100] Furthermore, the calculation formula of the weight value is:

[0101]

[0102] Where s(p) represents the number of suspected nodes on the detection path p, E(p) represents the set of all links on the path p, and n(l) is the number of times the selected detection path passes through the path.

[0103] When implementing it specifically, Figure 5 As shown, Figure 5 (a) shows the network topology node marking when the secondary fault judgment of the suspected fault node 5 is normal; Figure 5 (b) shows the network topology node markings when a secondary fault determination is performed on suspected faulty node 5 and the detection result is abnormal. This step performs secondary fault determination on as many suspected nodes as possible. In the network topology, there are multiple paths from the server to the suspected node, and there are multiple suspected nodes. Therefore, it is necessary to select the optimal path from the candidate fault determination paths. This secondary fault determination and optimal path selection improves the fault location success rate and reduces the location cost.

[0104] Define the weight w(p) of a candidate probe path p. For a selected probe path, the more times it traverses the path, the lower its weight. The more suspected nodes a probe path traverses, the higher its weight. This is calculated using the following formula. Here, s(p) represents the number of suspected nodes on probe path p. E(p) represents the set of all links on path p, and n(l) represents the number of times the selected probe path traverses the path.

[0105]

[0106] The faulty node determination method generates a new detection path set (DPS) for the remaining suspected nodes and selects appropriate detection paths until the detection path set is empty or all suspected nodes have been determined. The algorithm input is the node labeling results from step 3, namely the normal node set (NNS), the faulty node set (FNS), the suspected node set (SNS), and the unknown node set (UNS). The algorithm output is the updated sets.

[0107] At the beginning of the algorithm, it is necessary to determine whether the size of the current suspected node set SNS is empty. If it is not empty, the algorithm first traverses the suspected node set SNS, calculates the detection path from the root node to each suspected node SNS, generates a candidate detection path set DPS, and calculates the weight w(p) of each candidate path.

[0108] If the current candidate detection path set is empty, the SNS node in the suspected node set is determined to be an unknown node and added to the UNS. At the same time, the loop ends early and the result is returned.

[0109] Otherwise, the algorithm iteratively selects the candidate path with the highest weight for detection. If detection is successful, the suspected node is added to the normal nodes. If detection fails and the suspected node value of the path is 1, the suspected node is determined to be a faulty node. If the suspected node value of the path is not 1, the end node of the path is changed to an unknown node and marked. If the marked node's parent node is a normal node, the marked node is changed to a faulty node.

[0110] After a subset of faulty nodes is identified, they are added to the faulty node set and candidate detection paths are regenerated for the remaining suspected nodes. This completes the determination of the suspected node set and outputs the updated status of each node. Ultimately, the fault is located and a fault location result is generated.

[0111] The IPTV network fault location method provided in this embodiment uses a probe selection method to utilize the probe detection range, combined with the network topology node weights, to calculate the benefit of each probe node. The terminal node with the maximum benefit is iteratively selected to start the deployed probe software and issue a measurement task. Based on the probe fault detection results, the node types on the detection path are first classified. Then, an adaptive fault node determination method is used to iteratively select the optimal detection path and determine the fault of the suspected node. This fault location method can improve the success rate of node fault location and reduce the cost of fault location while ensuring detection coverage.

[0112] The units involved in the embodiments described in this disclosure may be implemented by software or hardware.

[0113] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0114] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for locating an IPTV network fault, characterized in that: include: Step 1: define the target network as an undirected connected graph, define the detection range of a set of probes at the terminal node location, define the load of each node in the target network, and define the benefit function of each node; The step 1 specifically includes: Step 1.1: Define an undirected connected graph G(L, V, E) based on the topology of the target network, where L represents the set of terminal leaf nodes at the bottom layer, V represents the set of intermediate nodes in the graph excluding the terminal nodes, and E represents the set of paths between nodes in two adjacent layers. Step 1.2: Define the detection path DP of the probe at the terminal node i i , and accordingly define the sum of the nodes that the detection path of the probe passes through and the isolated nodes associated with it, and then take the union of all detection ranges of a group of probes as the detection range DR of a group of probes; Step 1.3: Take the node i as the root node and take the ratio of the number of leaf nodes it contains to the total number of network nodes N as its load W. j ; Step 1.4, define the profit function of terminal node i as Among them, DP i Indicates the detection path of the probe at the location of node i, IN j represents an isolated node on the detection path, DR represents the detection range of a group of probes, and the denominator represents the load sum of the detection path; Step 2: Based on the undirected connectivity graph, detection range, load of each node, and benefit function, iteratively select the location with the maximum benefit in each node as the starting probe to form a node location set; Step 3: Based on the end-to-end measurement results of each startup probe in the node location set, the node types on the detection path are marked to generate a detection result set, where the detection result set includes a normal node set, a faulty node set, a suspected node set, and an unknown node set; Step 4: Generate a new detection path set for the suspected node set and select a suitable detection path until the detection path set is empty or all suspected nodes are determined and the newly detected faulty node is added to the faulty node set to generate a fault location result.

2. The method according to claim 1, characterized in that , the step 2 specifically includes: Step 2.1: According to the network topology G, traverse all intermediate nodes and calculate the intermediate node load W i ; Step 2.2, traverse all terminal leaf nodes and calculate the detection path DP of each candidate probe position i , based on these detection paths, calculate the isolated nodes IN on the detection path j And the detection path load sum, get the detection range DR of the probe. If there are multiple paths from the probe to the root node, take the path with the smallest node load sum; Step 2.3: The algorithm loops for a preset number of times, and each time it selects the location with the highest current profit to start the probe. That is, the algorithm iteratively calculates the profit value V of each leaf node. i And select the node with the largest benefit. After each probe position is selected and added to the startup probe position set PS, it is deleted from the silent probe set, and then the benefit value of the remaining terminal nodes is recalculated until the number of selected probes is the same as the preset number, and the node position set is obtained.

3. The method according to claim 2, characterized in that , the step 3 specifically includes: The server activates the startup probes at the node locations based on the node location set and simultaneously issues distributed active detection tasks. The activated probes proactively connect to the server to measure network performance and monitor media quality, and then feed the data back to the server. The server analyzes and processes the information fed back by the probes and generates fault detection results based on the network topology. Determine the type of each node based on the fault detection results; If the fault detection result is normal, the nodes on the detection path are added to the normal node set; If the fault detection result is abnormal, the nodes on the detection path are added to the set of suspected nodes; If there is only one suspected node, it is determined to be a faulty node and added to the faulty node set; If a suspected node cannot be reached from any normal node, it is determined to be an unknown node and added to the unknown node set; If a node is not passed by any detection path, it is determined to be an unknown node and added to the unknown node set.

4. The method according to claim 3, characterized in that , the step 4 specifically includes: When the current suspected node set is not empty, traverse the suspected node set, calculate the detection path from the root node to each suspected node, generate a set of candidate detection paths, and calculate the weight w(p) of each candidate path; Select the candidate path with the highest weight for detection. If the detection is successful, the suspected node will be added to the normal node. If the detection fails and the suspected node on the path is 1, the suspected node is judged as a faulty node. If the suspected node on the path is not 1, the end node of the path is changed to an unknown node and marked; If the parent node of the marked node is a normal node, the marked node will be changed to a faulty node; After a preset number of faulty nodes are determined, they are added to the faulty node set, and candidate detection paths are regenerated and determined for the remaining suspected nodes. The updated status of each node is output to form the fault location result.

5. The method according to claim 4, characterized in that , the weight calculation formula is: Where s(p) represents the number of suspected nodes on the detection path p, E(p) represents the set of all links on the path p, and n(l) is the number of times the selected detection path passes through the path.

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