Wireless networking fault positioning method, device and system based on binary tree algorithm

Abstract

The invention discloses a wireless networking fault positioning method, device and system based on a binary tree algorithm, and relates to the technical field of communication network security, and the key points of the technical scheme are as follows: obtaining alarm information of a monitored network; judging whether the monitored network has a single-link fault or a multi-link fault according to the alarm information; if the fault is a single link fault, positioning the position of a fault point based on the topological structure of the monitored network, the alarm information and a pre-acquired binary decision tree model; if a multi-link fault occurs, decomposing the monitored network into a plurality of binary tree sub-networks; and for each binary tree sub-network, positioning the position of the fault point based on the topological structure of the binary tree sub-network, the alarm information and a pre-acquired binary decision tree model. By adopting the scheme provided by the invention, the timeliness and the accuracy of networking fault positioning are improved, and the safety and the reliability of networking communication are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network, and in particular to a wireless networking fault locating method based on a binary tree algorithm, a wireless networking fault locating device based on a binary tree algorithm, and a wireless networking application system. Background Art

[0002] Wireless networking is when devices are connected to the Internet and networked wirelessly. There are several typical network topologies for wireless networking, such as star network topology, ring network topology, and tree network topology. They are usually multi-level networks with a large number of network nodes. When one or several nodes (links) fail, other network nodes that are connected to or have a logical relationship with the failed node often send out alarm messages. It is difficult for humans to accurately and timely determine which node has a fault, which in turn causes maintenance personnel to be unable to eliminate network faults in a timely manner, seriously affecting the security and reliability of network communications. Summary of the invention

[0003] In order to overcome the above problems or partially solve the above problems, one of the purposes of the present invention is to provide a wireless networking fault location method and device based on a binary tree algorithm to improve the timeliness and accuracy of networking fault location and ensure the security and reliability of networking communications; the second purpose of the present invention is to provide a wireless networking application system to improve the fault tolerance of networking, the security and reliability of communications.

[0004] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a wireless networking fault location method based on a binary tree algorithm, the method comprising: obtaining alarm information of a monitored network; judging whether the monitored network has a single-link fault or a multi-link fault according to the alarm information; if it is a single-link fault, locating the fault point based on the topological structure of the monitored network, the alarm information and a pre-acquired binary decision tree model; if it is a multi-link fault, decomposing the monitored network into multiple binary tree sub-networks, any one of the binary tree sub-networks contains at most one faulty link, and any link in the monitored network is contained in only one of the binary tree sub-networks; for each binary tree sub-network, locating the fault point based on the topological structure of the binary tree sub-network, the alarm information and the pre-acquired binary decision tree model.

[0005] Based on the first aspect, in an embodiment of the present invention, the method for obtaining the binary decision tree model includes: counting all possible faults that may occur in the monitored network; traversing to obtain the manifestations in the corresponding alarm information when a fault occurs in the monitored network, and classifying the faults based on the manifestations; fitting the mapping relationship between the faults and the corresponding manifestations according to the fault categories, and establishing multiple binary decision tree models for different fault categories.

[0006] Based on the first aspect, in an embodiment of the present invention, the monitored network is decomposed into multiple binary tree sub-networks, including: A1, determining the maximum number of links M that a single binary tree sub-network can contain; A2, obtaining the node degree of each network node in the monitored network, and sorting all network nodes in the monitored network in descending order of the node degree; A3, taking the network node with the largest node degree as the root node; A4, if M>2, selecting the two network nodes with the largest node degree adjacent to the root node as the child nodes of the root node; A5, selecting the network node with the larger node degree from the two child nodes as the parent node, and selecting one or two network nodes with the largest node degree adjacent to the parent node as the child nodes. is the child node of the parent node; A6, repeat step A5 until the number of links in the generated binary tree subnetwork reaches N, where 2<N≤M; A7, from the remaining network nodes adjacent to the root node, select the two network nodes with the largest node degrees as the child nodes of the root node, and repeat step A5 until the number of links in the generated binary tree subnetwork reaches N; A8, repeat step A7 until all links directly connected to the root node are included in the generated binary tree subnetwork, and reselect the network node as the new root node according to the sorting; A9, repeat steps A4-A8 until any link in the monitored network is included in one of the binary tree subnetworks, and the decomposition of the monitored network is completed.

[0007] Based on the first aspect, in an embodiment of the present invention, the calculation formula of the maximum number of links M is as follows: ; In the formula, d represents the maximum number of faulty links in the monitored network, q represents the total number of links in the monitored network, Indicates rounding down; in, ; In the formula, E represents the set of network nodes in the monitored network. If a fault alarm occurs during the service transmission between any pair of nodes (a, b) in E, then The value of is recorded as 1, otherwise it is recorded as 0; Indicates that the monitored network is operating normally The value of Indicates the connection status between nodes (a, b).

[0008] Based on the first aspect, in an embodiment of the present invention, determining whether a monitored network has a single-link failure or a multi-link failure based on alarm information includes: obtaining the maximum value d of the number of faulty links in the monitored network; if d=1, determining that a single-link failure exists in the monitored network; if d>1, determining that a multi-link failure exists in the monitored network.

[0009] Based on the first aspect, in an embodiment of the present invention, the method further includes: for a binary tree sub-network, configuring a signal transmission module at a root node of the binary tree sub-network, configuring at least K signal monitoring modules at child nodes of the binary tree sub-network, and configuring at most one signal monitoring module at a single child node; Determine the faulty link based on the signal receiving status of each signal monitoring module; in, ; In the formula, Q represents the total number of links contained in the target binary tree subnetwork, represents rounding up, and K represents the number of configured signal monitoring modules in the binary tree subnetwork.

[0010] In a second aspect, the present invention provides a wireless networking fault locating device based on a binary tree algorithm, the device comprising: an acquisition module, used to acquire alarm information of a monitored network; a judgment module, used to judge whether a fault exists in the monitored network, and whether the monitored network belongs to a single-link fault or a multi-link fault, based on the alarm information; a first execution module, when a single-link fault exists in the monitored network, used to locate the fault point based on the topological structure of the monitored network, the alarm information and a pre-acquired binary decision tree model; a second execution module, when a multi-link fault exists in the monitored network, used to decompose the monitored network into multiple binary tree sub-networks, and locate the fault point based on the topological structure of the binary tree sub-networks, the alarm information and the pre-acquired binary decision tree model; wherein any binary tree sub-network contains at most one faulty link, and any link in the monitored network is contained in one and only one of the binary tree sub-networks.

[0011] In a third aspect, the present invention provides a wireless networking application system, including: IPRAN networking; a wireless networking fault locating device based on a binary tree algorithm as provided in the second aspect, used to locate the location of a fault in the IPRAN networking; a power supply module, used to power the equipment in the IPRAN networking and the wireless networking fault locating device.

[0012] Based on the third aspect, in an embodiment of the present invention, the core layer of the IPRAN networking is composed of a plurality of master station devices, and the plurality of master station devices serve as master and backup for each other.

[0013] Based on the third aspect, in an embodiment of the present invention, the power supply module includes a photovoltaic power generation unit, a wind power generation unit and an energy storage unit; wherein the photovoltaic power generation unit and the wind power generation unit are respectively connected to the energy storage unit, and the energy storage unit is used to power the equipment in the IPRAN network and the wireless networking fault location device.

[0014] Compared with the prior art, the present invention has at least the following advantages and beneficial effects: The wireless networking fault location method and device based on the binary tree algorithm provided by the present invention are matched with different fault location methods according to the different numbers of fault links in the fault network (monitored network), thereby ensuring the efficiency and accuracy of fault location. When there are multiple fault links in the fault network, missed detection can be avoided. In addition, the wireless networking application system provided by the present invention can improve the network's fault tolerance, the security and reliability of network communication by setting up multiple master station devices that are mutually master-slave in the core layer of the wireless network; its power supply module includes two clean energy power generation units, namely a photovoltaic power generation unit and a wind power generation unit, which can reduce the operating cost of the network and improve the reliability of the network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A schematic flow chart of a method for locating wireless network faults based on a binary tree algorithm; Figure 2 A schematic diagram of the binary tree network topology for assembling signal receiving / transmitting devices; Figure 3 The present invention is a structural schematic block diagram of a wireless networking fault location device based on a binary tree algorithm.

[0016] Icons: 1. Acquisition module; 2. Judgment module; 3. First execution module; 4. Second execution module. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0018] It should be noted that the terms "include" or "may include" used in various embodiments of the present invention indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present invention, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0019] In various embodiments of the present invention, the expression "or" or "at least one of B or / and C" includes any combination or all combinations of the words listed at the same time. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0020] It should be understood that, in the description of the present invention, “plurality” means two or more than two, unless otherwise clearly and specifically defined.

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0022] Please refer to Figure 1 This embodiment provides a wireless networking fault location method based on a binary tree algorithm, the method comprising: S1. Obtain alarm information of the monitored network; For example, the existing network management system can be used to obtain alarm information. The specific acquisition methods are as follows: 1) SNMP Trap: The network element reports in real time, so the alarm is timely and the alarm collection and processing are generally completed within 5 seconds. 2) SNMP Get: The network management system periodically polls the device status, so the setting of the alarm delay depends on the length of the polling cycle. Considering that too frequent SNMPGet operations will affect the load of the network element equipment, the polling cycle is generally set to 5 minutes. 3) Threshold alarm: An alarm generated when a performance indicator exceeds a certain threshold, such as CPU utilization alarm, optical power abnormal alarm, etc. Since the performance indicator needs to be observed for a certain period of time, the alarm delay is generally around 5 minutes.

[0023] S2. Determine whether a single link failure or a multi-link failure exists in the monitored network according to the alarm information; Specifically, the maximum value d of the number of faulty links in the monitored network is obtained; if d=0, there is no fault in the monitored network; if d=1, it is determined that a single link fault exists in the monitored network; if d>1, it is determined that multiple link faults exist in the monitored network (although the value of d here represents the number of suspected faulty links, the actual faulty link may still be 1, but in order to avoid missed detection, it is still considered that the monitored network has multiple link faults).

[0024] S3. If it is a single link failure, locate the fault point based on the topological structure of the monitored network, the alarm information and the pre-acquired binary decision tree model; Specifically, the method for obtaining the binary decision tree model includes: Step 1: Count all possible faults that may occur in the monitored network; Step 2: traverse and obtain the manifestations of the corresponding alarm information when a fault occurs in the monitored network, and classify the faults based on the manifestations; for example, a power failure of device A will cause a Node Down alarm, and at the same time, a DOWN alarm will appear in the port link status of the opposite device B directly connected to device A.

[0025] Step 3: According to the fault category, fit the mapping relationship between the fault and the corresponding manifestation, and establish multiple binary decision tree models for different fault categories. This can improve the training efficiency of the model, the accuracy of the model judgment, and the judgment efficiency (the fewer branches, the higher the judgment efficiency).

[0026] Since this embodiment is for single-link failures, when a single-link failure occurs in the monitored network, there are usually not many alarm messages, and it is easy to determine the type of failure through the alarm information, which can greatly narrow the scope of troubleshooting. For example, if an alarm message shows "Device A is offline", the cause of this type of failure may be power failure of device A, or other devices connected to it, or poor local network conditions, etc., which needs to be analyzed and confirmed in combination with other alarm information. For example, if the alarm information shows that a large number of devices in the same area as device A are offline, it can be determined that the cause of this failure is poor local network conditions (the cause of the poor network condition may be local power failure or base station failure, etc., which will not be further investigated here).

[0027] Preferably, a reasoning tree for accurate fault location is designed based on a binary decision tree, and its construction principle is: 1) Alarm importance is reported from the highest layer to the lowest layer. Upper layer device alarms take precedence over lower layer device alarms, device alarms take precedence over board alarms, and board alarms take precedence over port alarms. 2) The importance of primary alarms takes precedence over derived alarms; 3) The more clearly located the alarm is, the earlier it will be judged; 4) Physical alarms take precedence over logical alarms.

[0028] Judging from top to bottom step by step can achieve accurate positioning of faults.

[0029] S4. If it is a multi-link fault, the monitored network is decomposed into multiple binary tree sub-networks, any binary tree sub-network contains at most one faulty link, and any link in the monitored network is contained in only one of the binary tree sub-networks; for each binary tree sub-network, the location of the fault point is located based on the topological structure of the binary tree sub-network, the alarm information and the pre-acquired binary decision tree model.

[0030] In this step, the main idea is to decompose the monitored network containing multiple faulty links into multiple binary tree sub-networks, so that each binary tree sub-network contains at most one faulty link. In this way, the multi-link fault location problem can be converted into a single-link fault location problem, and then the method in step S3 can be applied to each binary tree sub-network to locate a single link fault (single fault point).

[0031] Specifically, the monitored network is decomposed into a plurality of binary tree sub-networks, including: A1. Determine the maximum number of links M that a single binary tree subnetwork can contain; A2. Obtain the node degree of each network node in the monitored network, and sort all network nodes in the monitored network in descending order of the node degree; A3. Take the network node with the largest node degree as the root node; A4. If M>2, the two network nodes with the largest node degrees adjacent to the root node are selected as the child nodes of the root node; if M=2, the two network nodes with the largest node degrees adjacent to the root node are selected as the child nodes of the root node to generate a binary tree sub-network; if M=1, the network node with the largest node degree adjacent to the root node is selected as the child node of the root node to generate a binary tree sub-network (this situation is more extreme and usually does not occur); A5. Select the network node with the larger node degree from the two child nodes as the parent node, and select one or two network nodes with the largest node degree adjacent to the parent node as the child nodes of the parent node; A6. Repeat step A5 until the number of links in the generated binary tree subnetwork reaches N, where 2<N≤M. Limited by the total number of network nodes in the monitored network and the subnetwork decomposition rule (any binary tree subnetwork contains at most one faulty link, and any link in the monitored network is contained in only one binary tree subnetwork), it cannot be guaranteed that the number of links in each binary tree subnetwork can reach M. However, for the binary tree subnetworks generated in the early stage of decomposition, the number of links in the binary tree subnetworks can reach M as much as possible, which can reduce the number of binary tree subnetworks generated and improve the efficiency of fault location.

[0032] A7. Select two network nodes with the largest node degrees from the remaining network nodes adjacent to the root node as child nodes of the root node, and repeat step A5 until the number of links in the generated binary tree subnetwork reaches N; A8, repeating step A7 until all links directly connected to the root node are included in the generated binary tree subnetwork, and reselecting a network node as a new root node according to the sorting; A9. Repeat steps A4-A8 until any link in the monitored network is included in one of the binary tree subnetworks, and the decomposition of the monitored network is completed.

[0033] Exemplarily, in this embodiment, it is assumed that multiple faulty links in the monitored network obey Bernoulli distribution, and the calculation formula of the maximum number of links M is as follows: , where d represents the maximum number of faulty links in the monitored network, and q represents the total number of links in the monitored network. It means rounding down; that is, when the total number of links in the monitored network is q and the maximum number of faulty links is d, the probability that any subnetwork with no more than M links contains at most one faulty link is infinitely close to 1.

[0034] in, , where E represents the set of network nodes in the monitored network. If a fault alarm occurs during the service transmission between any pair of nodes (a, b) in E, then The value of is recorded as 1, otherwise it is recorded as 0; Indicates that the monitored network is operating normally The value of Indicates the connection status between nodes (a, b).

[0035] It should be understood that if a fault alarm occurs during the service transmission between nodes (a, b), it means that there is a fault in the connection between nodes (a, b). It is recorded as 1, otherwise it is recorded as 0.

[0036] In the above embodiment, the location of the fault mainly depends on the analysis and reasoning of the alarm information by the binary decision tree model, and the generation of the alarm information depends on the monitoring trigger of the monitoring device. Therefore, this embodiment provides a method for determining the faulty link by using the monitoring device, which is as follows: B1. For the binary tree sub-network, a signal transmission module is configured at the root node of the binary tree sub-network, at least K signal monitoring modules are configured at the child nodes of the binary tree sub-network, and at most one signal monitoring module is configured at a single child node; B2. Determine the faulty link based on the signal receiving status of each signal monitoring module; in, ; In the formula, Q represents the total number of links contained in the target binary tree subnetwork, represents rounding up, and K represents the number of configured signal monitoring modules in the binary tree subnetwork.

[0037] For example, Figure 2As shown in the figure, it is a schematic diagram of a binary tree topology containing 6 nodes and 5 links (the arrows in the figure indicate the direction of signal transmission); a signal transmission module is configured at node 1, and a signal monitoring module is configured at nodes 4, 5, and 6 respectively. If a link fails, the monitoring signal on the corresponding link and the monitoring signal on its export link will be interrupted. Specifically, if link L1 is a faulty link, signal monitoring modules A, B, and C cannot obtain signals; if link L2 is a faulty link, signal monitoring module A can obtain signals, and signal monitoring modules B and C cannot obtain signals; if link L3 is a faulty link, signal monitoring modules B and C can obtain signals, and signal monitoring module A cannot obtain signals; if link L4 is a faulty link, signal monitoring modules A and B can obtain signals, and signal monitoring module C cannot obtain signals; if link L5 is a faulty link, signal monitoring modules A and C can obtain signals, and signal monitoring module B cannot obtain signals.

[0038] After the faulty link is identified, the fault scope can be greatly reduced, providing accurate alarm information for the binary decision tree model, which helps to improve the judgment accuracy of the binary decision tree model.

[0039] like Figure 3 As shown, this embodiment provides a wireless networking fault location device based on a binary tree algorithm, the device comprising: Acquisition module 1, used to obtain alarm information of the monitored network; The judgment module 2 is used to judge whether there is a fault in the monitored network according to the alarm information, and whether the monitored network is a single-link fault or a multi-link fault; The first execution module 3 is used to locate the fault point based on the topological structure of the monitored network, the alarm information and the pre-acquired binary decision tree model when a single link fault occurs in the monitored network; The second execution module 4 is used to decompose the monitored network into multiple binary tree sub-networks when there are multi-link failures in the monitored network, and locate the fault point based on the topological structure of the binary tree sub-networks, alarm information and a pre-acquired binary decision tree model; wherein any binary tree sub-network contains at most one faulty link, and any link in the monitored network is contained in only one of the binary tree sub-networks.

[0040] This embodiment also provides a wireless networking application system, including: IPRAN networking; A wireless networking fault location device based on a binary tree algorithm is used to locate the location of a fault in an IPRAN network; The power supply module is used to supply power to the equipment in the IPRAN network and the wireless networking fault locating device.

[0041] Furthermore, the core layer of the IPRAN networking is composed of a plurality of master station devices, and the plurality of master station devices serve as master and backup for each other.

[0042] Furthermore, the power supply module includes a photovoltaic power generation unit, a wind power generation unit and an energy storage unit; wherein the photovoltaic power generation unit and the wind power generation unit are respectively connected to the energy storage unit, and the energy storage unit is used to power the equipment in the IPRAN network and the wireless networking fault locating device.

[0043] The wireless networking application system provided in this embodiment can improve the network's fault tolerance, security and reliability of network communications by setting up multiple master station devices that serve as master and backup for each other in the core layer of the wireless network; its power supply module includes two clean energy power generation units, namely a photovoltaic power generation unit and a wind power generation unit, which can reduce the operating cost of the network and improve the reliability of the network operation.

[0044] The device and system provided in the embodiments of the present invention may be used to execute the methods in the above embodiments, which will not be described in detail here.

[0045] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wireless networking fault location method based on a binary tree algorithm, characterized in that: The method comprises: Obtain alarm information of the monitored network; Determine whether the monitored network has a single link failure or multiple link failures based on the alarm information; If it is a single-link fault, the fault point is located based on the topological structure of the monitored network, the alarm information and the pre-acquired binary decision tree model; If it is a multi-link failure, the monitored network will be decomposed into multiple binary tree sub-networks. Any binary tree sub-network contains at most one faulty link, and any link in the monitored network is contained in only one of the binary tree sub-networks. For each binary tree sub-network, the location of the fault point is located based on the topological structure of the binary tree sub-network, the alarm information and the pre-acquired binary decision tree model.

2. A wireless networking fault location method based on a binary tree algorithm according to claim 1, characterized in that: The method for obtaining the binary decision tree model includes: Collect statistics on all possible faults in the monitored network; Traversing and obtaining the manifestation forms in the corresponding alarm information when a fault occurs in the monitored network, and classifying the fault based on the manifestation forms; The mapping relationship between faults and corresponding manifestations is fitted according to the fault categories, and multiple binary decision tree models for different fault categories are established.

3. The method for locating wireless network faults based on a binary tree algorithm according to claim 1, characterized in that: The monitored network is decomposed into a plurality of binary tree sub-networks, including: A1. Determine the maximum number of links M that a single binary tree subnetwork can contain; A2. Obtain the node degree of each network node in the monitored network, and sort all network nodes in the monitored network in descending order of the node degree; A3. Take the network node with the largest node degree as the root node; A4. If M>2, select the two network nodes with the largest node degrees adjacent to the root node as the child nodes of the root node; A5. Select the network node with the larger node degree from the two child nodes as the parent node, and select one or two network nodes with the largest node degree adjacent to the parent node as the child nodes of the parent node; A6. Repeat step A5 until the number of links in the generated binary tree subnetwork reaches N, where 2<N≤M; A7. Select two network nodes with the largest node degrees from the remaining network nodes adjacent to the root node as child nodes of the root node, and repeat step A5 until the number of links in the generated binary tree subnetwork reaches N; A8, repeating step A7 until all links directly connected to the root node are included in the generated binary tree subnetwork, and reselecting a network node as a new root node according to the sorting; A9. Repeat steps A4-A8 until any link in the monitored network is included in one of the binary tree subnetworks, and the decomposition of the monitored network is completed.

4. A method for locating wireless network faults based on a binary tree algorithm according to claim 3, characterized in that: The calculation formula of the maximum number of links M is as follows: , where d represents the maximum number of faulty links in the monitored network, and q represents the total number of links in the monitored network. Indicates rounding down; in, , where E represents the set of network nodes in the monitored network. If a fault alarm occurs during the service transmission between any pair of nodes (a, b) in E, then The value of is recorded as 1, otherwise it is recorded as 0; Indicates that the monitored network is operating normally The value of Indicates the connection status between nodes (a, b).

5. The method for locating wireless network faults based on a binary tree algorithm according to claim 1, characterized in that: The determining, according to the alarm information, whether a single link failure or a multi-link failure exists in the monitored network includes: Obtain the maximum value d of the number of faulty links in the monitored network; If d=1, it is determined that a single link failure exists in the monitored network; If d>1, it is determined that there are multiple link failures in the monitored network.

6. A method for locating wireless network faults based on a binary tree algorithm according to claim 1, characterized in that: The method further comprises: For the binary tree sub-network, a signal transmission module is configured at the root node of the binary tree sub-network, at least K signal monitoring modules are configured at the child nodes of the binary tree sub-network, and at most one signal monitoring module is configured at a single child node; Determine the faulty link based on the signal receiving status of each signal monitoring module; in, ; In the formula, Q represents the total number of links contained in the target binary tree subnetwork, represents rounding up, and K represents the number of configured signal monitoring modules in the binary tree subnetwork.

7. A wireless networking fault location device based on a binary tree algorithm, characterized in that: The device includes: An acquisition module, used to obtain alarm information of the monitored network; A judgment module is used to judge whether there is a fault in the monitored network according to the alarm information, and whether the monitored network is a single-link fault or a multi-link fault; A first execution module, when a single link failure occurs in the monitored network, is used to locate the location of the fault point based on the topological structure of the monitored network, the alarm information and the pre-acquired binary decision tree model; The second execution module is used to decompose the monitored network into multiple binary tree sub-networks when there are multi-link failures in the monitored network, and locate the fault point based on the topological structure of the binary tree sub-network, alarm information and a pre-acquired binary decision tree model; wherein any binary tree sub-network contains at most one faulty link, and any link in the monitored network is contained in only one of the binary tree sub-networks.

8. A wireless networking application system, characterized in that: The system includes: IPRAN networking; A wireless networking fault locating device based on a binary tree algorithm as claimed in claim 7, used to locate the location of a fault in an IPRAN network; as well as The power supply module is used to supply power to the equipment in the IPRAN network and the wireless networking fault locating device.

9. A wireless networking application system according to claim 8, characterized in that: The core layer of the IPRAN network is composed of multiple master station devices, and the multiple master station devices serve as master and backup for each other.

10. A wireless networking application system according to claim 8, characterized in that: The power supply module includes a photovoltaic power generation unit, a wind power generation unit and an energy storage unit; wherein the photovoltaic power generation unit and the wind power generation unit are respectively connected to the energy storage unit, and the energy storage unit is used to supply power to the equipment in the IPRAN network and the wireless networking fault locating device.

Images