CN113452537B - Model-Based Fault Location Method and Device - Google Patents

Abstract

The invention discloses a model-based fault location method and device, wherein the method includes: constructing a customer-service-resource equipment model; wherein the customer-service-resource equipment model records the associated topological relationship among customers, services and resource equipment ;Receive the declaration request with unavailable business data triggered by the customer; traverse the resource equipment in the customer-business-resource equipment model, and for any resource equipment, simulate whether the resource equipment is available to customers and services in the simulated fault state The prediction result; calculate the matching degree according to the prediction result and unavailable business data, and locate the faulty resource device according to the calculated matching degree. According to the customer-service-resource equipment model based on the construction of the present invention, the positioning of the faulty resource equipment is completed by simulating the declaration request of the unavailable service triggered by the customer, which reduces the dependence on the technical personnel's own ability and improves the efficiency of manual investigation. Processing efficiency.

Description

Model-Based Fault Location Method and Device

technical field

The invention relates to the technical fields of data services and computer software, in particular to a model-based fault location method and device.

Background technique

Internet Data Center (IDC for short) is a service with complete equipment (including high-speed Internet access bandwidth, high-performance local area network, safe and reliable computer room environment, etc.), professional management, and perfect application platform. On the basis of this platform, IDC service providers provide customers with Internet basic platform services (such as Internet bandwidth, virtual private network, server hosting, virtual hosting, etc.) and various value-added services (such as domain name system services, load balancing systems, data storage and backup, data analysis and processing, etc.).

During the IDC schedule maintenance process, customers will receive a report that the service is unavailable. For this report, technicians need to find the fault location in the network in time, that is, locate the fault, repair or replace the faulty equipment and components, and Restore service to the business as soon as possible. In the existing technology, the fault location reported by the customer mainly depends on the maintenance experience and technical level of the technicians. According to the service access location of the IDC customer, the equipment that may be faulty is manually checked one by one, and it is necessary to check or log in the equipment one by one remotely. On-site inspection, the positioning cycle is long, and the accuracy of positioning depends on the ability of technicians.

Contents of the invention

In view of the above problems, the present invention is proposed to provide a model-based fault location method and device that overcomes the above problems or at least partially solves the above problems.

According to one aspect of the present invention, a model-based fault location method is provided, comprising:

Build a customer-business-resource equipment model; among them, the customer-business-resource equipment model records the associated topological relationship among customers, business and resource equipment;

Receive a declaration request triggered by a customer that carries unavailable business data;

Traverse the resource equipment in the customer-business-resource equipment model, and for any resource equipment, simulate the prediction result of whether the resource equipment is available to customers and services in the simulated fault state;

Calculate the matching degree based on the predicted result and unavailable service data, and locate the faulty resource device based on the calculated matching degree.

According to another aspect of the present invention, a model-based fault location device is provided, which includes:

The building block is suitable for constructing a customer-service-resource equipment model; wherein, the customer-service-resource equipment model records the associated topological relationship among customers, services and resource equipment;

The receiving module is adapted to receive a declaration request triggered by a client and carrying unavailable service data;

The simulation module is suitable for traversing the resource equipment in the customer-service-resource equipment model, and for any resource equipment, simulates and obtains a prediction result of whether the resource equipment is available to customers and services in a simulated fault state;

The matching module is adapted to calculate the matching degree according to the predicted result and unavailable service data, and locate the faulty resource device according to the calculated matching degree.

According to another aspect of the present invention, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete mutual communication through the communication bus communication;

The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the above model-based fault location method.

According to still another aspect of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the above-mentioned model-based fault location method.

According to the model-based fault location method and device of the present invention, a customer-service-resource equipment model is constructed; wherein, the customer-service-resource equipment model records the associated topological relationship among customers, services, and resource equipment; receiving customer triggers carries Declaration request for unavailable business data; traverse the resource equipment in the customer-business-resource equipment model, and for any resource equipment, simulate the prediction result of whether the resource equipment is available to customers and services in the simulated fault state; according to the forecast Calculate the matching degree based on the judgment result and unavailable service data, and locate the faulty resource device based on the calculated matching degree. According to the customer-service-resource equipment model constructed in the present invention, the location of the faulty resource equipment is completed by simulating the declaration request of the unavailable service triggered by the customer, which reduces the dependence on the technical personnel's own ability and improves the efficiency of manual investigation. Processing efficiency.

The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable , the specific embodiments of the present invention are enumerated below.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiment. The drawings are only for the purpose of illustrating a preferred embodiment and are not to be considered as limiting the invention. Also throughout the drawings, the same reference numerals are used to designate the same parts. In the attached picture:

FIG. 1 shows a flowchart of a model-based fault location method according to an embodiment of the present invention;

Fig. 2 shows a schematic diagram of a client-service-resource device model according to an embodiment of the present invention;

Fig. 3 shows a schematic diagram of simulating a resource device failure by a client-service-resource device model according to an embodiment of the present invention;

FIG. 4 shows a functional block diagram of a model-based fault location device according to an embodiment of the present invention;

Fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

Fig. 1 shows a flowchart of a model-based fault location method according to an embodiment of the present invention. As shown in Figure 1, the model-based fault location method specifically includes the following steps:

Step S101, constructing a client-service-resource device model.

In the existing technology, IDC fault location mostly adopts the method of manual inspection by technicians. When the operation and maintenance personnel receive the declaration that the IDC customer service is unavailable, they manually handle it, and rely more on the experience of the operation and maintenance personnel to check the equipment. The positioning time is long. And because there is no direct relationship between the equipment itself and the customer's business, it is necessary to check the equipment one by one during the investigation, which takes a long time.

Based on the above problems, this embodiment constructs a client-service-resource device model, which records the associated topological relationship among clients, services and resource devices. Specifically, based on the network topology discovery technology, the resource device information of the IDC Internet data center network is scanned and obtained. Resource device information includes resource device type, north-south link relationship, network type, etc. For example, scan the entire network to obtain all network resource devices in the IDC network, including port devices, access devices, convergence devices, and routing devices, and also obtain the north-south link relationship and network type between resource devices. Network type such as star network, hierarchical network, etc. After using the network topology discovery technology to obtain a resource device, read the next-hop address of the direct route in the routing table, and then obtain the subnet mask of these addresses from the address table, and calculate it through the gateway address and subnet mask Get the address range of the subnet, and then scan the addresses in the address range to discover all resource devices in the IDC network. According to the resource equipment information, construct the resource equipment operation and maintenance model. Specifically shown in Figure 2. The resource device operation and maintenance model includes port devices, access devices, aggregation devices, and routing devices, as well as north-south multi-level link connections between resource devices.

As for the network topology discovery technology, it specifically includes discovering devices and discovering link relationships. There are two device discovery methods: seed address diffusion and network segment scanning, which are automatically discovered by the network. You can use one of them, or use both methods together. Specific implementation of the seed address diffusion method: If you have a good understanding of the current network address distribution and can clearly know the core device information of the network, you can set several core devices as seed addresses. The automatic network discovery function will use these seed addresses as the starting point, and spread outward step by step by collecting and analyzing relevant information of the device. The seed address diffusion method is suitable for all network devices with complete device protocol settings and unobstructed access to the monitoring server. If there are multiple unconfigured protocols in the network structure or network nodes that cannot be accessed by the monitoring server, you may encounter obstacles during the expansion process and cannot continue to expand and explore. The advantage of the seed address spreading method is that all detection addresses are derived from the actual information of the resource device, which belongs to targeted address detection, and will not waste time due to redundant detection of empty addresses; the disadvantage is that it is necessary to clearly grasp the current The network structure and equipment protocol settings and connectivity requirements are complete. The specific implementation of the network segment scanning method: If you don’t know the address settings of the current network devices very well, and you can’t guarantee that the protocol settings of all devices are correct or you can’t be sure that the monitoring server can access all monitoring devices without barriers, you can set several The network segment roughly includes the devices to be monitored. The network auto-discovery function will first use all the addresses in the seed network segment as the starting addresses, and then use these starting addresses to gradually expand outward. The advantage of the network segment scanning method is that it can effectively bypass inaccessible device nodes and comprehensively discover devices by network segment coverage; at the same time, many empty address detection behaviors will be generated by network coverage, which will generate additional time consumption. The discovery interval is used to limit which ip addresses are tested during the process of address expansion in automatic discovery. If the automatic discovery function encounters an address outside the address range during the address expansion process, it will ignore this address and will not perform protocol testing and data collection. Address constraints that belong to the nature of the whitelist. The shielding interval is just the opposite of the discovery interval. Addresses within the interval will be ignored and belong to the blacklist setting. By adopting the method of setting the seed network segment together with the discovery interval and the shielding interval, the comprehensive discovery of network segment coverage can be guaranteed, and the waste of time for useless address space detection can be avoided. Link discovery includes the following methods: 1) Routing protocol analysis, the automatic discovery function can analyze the relationship between physical interfaces at both ends of the routing table entry according to the routing table data of the device, so as to discover the link between the devices. 2) The cdp temporary protocol, the automatic discovery function collects the data of the cdp temporary protocol for the equipment of the Cisco manufacturer, and calculates the interface connection relationship of the link layer between the Cisco equipment. 3) The ndp temporary protocol, the automatic discovery function collects the data of the ndp temporary protocol for equipment such as Huawei, and calculates the interface connection relationship of the link layer between Huawei devices. 4) lldp Ad hoc protocol, lldp ad hoc protocol is a cross-manufacturer link layer neighbor discovery protocol. If the lldp protocol is enabled for the device, the automatic discovery function can collect and analyze the lldp protocol to calculate the link layer interface relationship between the devices. During specific implementation of the above various manners, an appropriate implementation manner shall be selected according to the actual situation, which is not limited here.

After obtaining the resource device operation and maintenance model, obtain the customer information, business information, port resource device information of the IDC network, and network north-south traffic information to build a business operation model. As shown in Figure 2, the business operation model includes customer information, business information, port resource device information, and mutual north-south multi-level link connections. Here, the customer information, business information, and port resource device information of the IDC network can be obtained through the IDC operation platform, etc., and the business operation model can be constructed according to the corresponding relationship between customer information, business information, port resource device information, and north-south traffic. Form a business operation model with customers as the root, business as the branch, and port resource equipment as the leaf.

The construction sequence of the above resource equipment operation and maintenance model and business operation model is not specifically limited, and can be generated according to the actual implementation situation.

After the resource equipment operation and maintenance model and business operation model are constructed, the same port resource equipment between the resource equipment operation and maintenance model and the business operation model is used as the joint point, and combined processing is performed to obtain the customer-service-resource equipment model, as shown in Figure 2 shown.

Step S102, receiving a declaration request triggered by a client and carrying unavailable service data.

When a client makes a declaration request for unavailable service data, the client's declaration request is received, and the unavailable service data carried by it is obtained. Wherein, the unavailable service data includes unavailable services and current customer information.

Step S103, traversing the resource equipment in the customer-service-resource equipment model, and for any resource equipment, simulate to obtain the prediction result of whether the resource equipment is available to customers and services in the simulated failure state.

After receiving the unavailable business data, this embodiment simulates the faulty equipment, analyzes and conducts it according to the impact of the north-south link simulation failure in the customer-business-resource equipment model, and finally obtains the impact on customers and business , so that the unavailable service data corresponding to the simulated fault resource device can be determined, that is, the prediction result of whether the resource device is available to customers and services under the simulated fault state can be obtained.

Specifically, all resource devices in the client-service-resource device model are traversed, and for any resource device, the resource device is simulated to be in a simulated fault state. As shown in FIG. 3 , for example, the resource device (the rightmost access device in FIG. 3 is in a simulated fault state for illustration) is marked in a coloring manner as a simulated fault state. Then set the northbound links of the resource equipment in the customer-service-resource equipment model to a simulated failure state, as shown in Figure 3, mark the four northbound links of the access equipment on the far right with coloring to simulate a fault condition. After determining the simulated fault state of the link connection, set the state of other resource devices connected northbound by each link connection in the simulated fault state in turn, and obtain a forecast of whether the customer and business are available based on the state of other resource devices. Judgment result. For other resource devices connected northbound by each link in the simulated fault state, it can be directly extracted according to the customer-service-resource device model, and at the same time, the operation data of other resource devices can be obtained by detecting other resource devices. For any other resource device, the status of the other resource device is determined according to the operating data of the other resource device and/or the status of the southbound link of the other resource device. If the operating data of other resource devices is normal and the status of all southbound links of other resource devices is normal, then it is determined that the status of other resource devices is normal. If the operating data of other resource devices is faulty, or the status of all southbound link connections of other resource devices is a simulated fault state, then it is determined that the status of other resource devices is a simulated fault state. In the case that the operating data of other resource devices is normal, but the status of all its southbound link connections is a simulated fault state, considering the conduction influence of the first southbound link connection status, it is also determined that the status of other resource devices The state is a simulated fault state.

According to the status of other resource devices, if the status of other resource devices is a simulated fault state, set the northbound link connection of other resource devices to a simulated fault state. Recycle the above steps, go northward one by one, and then obtain the status of other resource devices connected to the northbound connection of each link in the simulated fault state, until the status of the business is confirmed in the northbound direction, and the pre-judgment result of whether the customer and business are available can be obtained. As shown in Figure 3, set the two services on the right to the predicted unavailable state, and terminate the loop. Here, when the unavailable status of the pre-judged service completely matches the unavailable service data carried in the declaration request, the loop can be terminated. If multiple faulty resource devices are considered, the above process can be repeated until they are completely matched.

According to the states of other resource devices, if the states of other resource devices are in normal state, then set the northbound link connection of other resource devices to be in normal state.

Optionally, before performing this step in this embodiment, the corresponding associated resource device may be determined from unavailable services to the south according to the declaration request and the client-service-resource device model. Detect the associated resource device to see if it is in a fault state. If yes, directly repair the associated resource device. If it is detected that the associated resource device is not in a fault state, perform this step to simulate the prediction result of whether the resource device is available to customers and services in the simulated fault state to locate the faulty resource device.

Step S104, calculate the matching degree according to the prediction result and the unavailable service data, and locate the faulty resource device according to the calculated matching degree.

According to the service prediction availability status, service prediction unavailability status and unavailable service data contained in the prediction result, the matching degree of the prediction result is calculated. The matching degree includes data such as the accuracy rate of the prediction result, the precision rate of the prediction result, and the recall rate of the prediction result. The calculation of the matching degree is a two-category pre-judgment task for whether the customer service is available. The customer service is divided into two categories according to whether it is available: 0 (available) and 1 (unavailable). There are 4 situations in the following table:

Customer declares service unavailable: 1 Customers not declaring business unavailable: 0 Business prediction unavailable status: 1 TP (True Positive) FP (False Positive) Available status of business prediction: 0 FN (False Negative) TN (True Negative)

Table 1

Among them, the accuracy rate of the prediction result is specifically: the proportion of the number of services that correctly predict the service prediction available state and the service prediction unavailable state in the prediction result to the total number of services. The total number of services includes the number of services that are correctly predicted to be available and unavailable, and the number of services that are incorrectly predicted to be available and unavailable. As shown in Table 1, the number of correctly predicted services in the available state is TN, the number of correctly predicted services in the unavailable state is TP, the number of wrongly predicted services in the available state is FN, and the wrong The number of services judged to be unavailable is FP. TP+FP+FN+TN=total business number. Prediction result accuracy rate = (TP+TN)/(TP+FP+FN+TN)*100%. In contrast, the prediction result error rate=100%-accuracy rate.

The accuracy rate of the prediction result is specifically: the proportion of the number of services in the predicted unavailable state that are correctly predicted in the prediction results to the number of services that are predicted to be in the unavailable state in the prediction results. Prediction result accuracy rate = TP/(TP+FP)*100%.

The recall rate of the prediction result is specifically: the proportion of the number of correctly predicted services in the predicted unavailable state in the prediction results to the unavailable business data. Pre-judgment result recall rate = TP/(TP+FN)*100%.

Comprehensively consider the precision rate of the prediction result and the recall rate of the prediction result, and use the harmonic mean of the two to obtain the average value of the precision rate of the prediction result and the recall rate of the prediction result = 2*precision rate*recall rate/(precision rate+recall Rate).

According to the prediction result accuracy rate, prediction result precision rate, prediction result recall rate and other data obtained from the above calculations, the resource device with the highest matching degree is determined as the fault setting resource. The highest matching degree specifically refers to the highest prediction result accuracy rate, or when the prediction result accuracy rates of multiple resource devices are equal, the highest matching degree is the highest average prediction result precision rate and prediction result recall rate.

Locate the resource device with the highest matching degree as the faulty resource device, detect it, and repair it in time.

According to the model-based fault location method provided by the present invention, a client-service-resource device model is constructed; wherein, the client-service-resource device model records the associated topological relationship among clients, services and resource devices; Use the business data declaration request; traverse the resource equipment in the customer-business-resource equipment model, and for any resource equipment, simulate the prediction result of whether the resource equipment is available to customers and services in the simulated fault state; according to the prediction The result and unavailable business data calculate the matching degree, and locate the faulty resource device according to the calculated matching degree. According to the customer-service-resource equipment model constructed in the present invention, the location of the faulty resource equipment is completed by simulating the declaration request of the unavailable service triggered by the customer, which reduces the dependence on the technical personnel's own ability and improves the efficiency of manual investigation. Processing efficiency. Furthermore, if there are multiple faulty resource devices, the present invention sorts these resource devices according to the degree of matching, which is convenient for technicians to check one by one according to the ranking and quickly repair the faulty resource devices.

Fig. 4 shows a functional block diagram of a model-based fault location device according to an embodiment of the present invention. As shown in Figure 4, the model-based fault location device includes the following modules:

The construction module 410 is suitable for constructing a customer-service-equipment resource model; wherein, the customer-service-equipment resource model records the associated topological relationship among customers, services and equipment resources;

The receiving module 420 is adapted to receive a declaration request triggered by a user and carrying unavailable service data;

The simulation module 430 is suitable for traversing the equipment resources in the customer-business-equipment resource model, and for any equipment resource, simulates and obtains the prediction result of whether the equipment resource is available to the customer and the business under the simulated failure state;

The matching module 440 is adapted to calculate the matching degree according to the predicted result and unavailable service data, and locate the faulty equipment resource according to the calculated matching degree.

Optionally, building block 410 further includes:

The first construction unit 411 is adapted to scan and obtain resource device information of the IDC Internet data center network; resource device information includes resource device type, north-south link relationship and network type; resource device includes port device, access device, convergence device and routing Equipment; construct a resource equipment operation and maintenance model based on resource equipment information; among them, the resource equipment operation and maintenance model includes port equipment, access equipment, aggregation equipment and routing equipment, and north-south multi-level link connections between various resource equipment;

The second construction unit 412 is adapted to acquire customer information, business information, port resource device information of the IDC network, and north-south flow information of the network, and construct a business operation model; the business operation model includes customer information, business information and port resource device information , and the mutual north-south multi-level link connection;

The third construction unit 413 is adapted to combine the same port resource devices between the resource device operation and maintenance model and the service operation model to obtain a client-service-resource device model.

Optionally, the device also includes:

The detection and repair module 450 is adapted to determine the associated resource device corresponding to the unavailable service according to the declaration request and the client-service-resource device model; detect whether the associated resource device is in a fault state; if so, repair the associated resource device.

Optionally, the simulation module 430 is further adapted to:

Traverse the resource equipment in the customer-business-resource equipment model, and for any resource equipment, simulate and set the resource equipment to a simulated failure state;

Set each northbound link connection of the resource device in the customer-business-resource device model to a simulated fault state;

Set the state of other resource devices in the north direction of each link that simulates the failure state in turn, and obtain the prediction result of whether the customer and business are available according to the state of other resource devices.

Optionally, the simulation module 430 is further adapted to:

Extract other resource devices in the north direction of each link in the simulated fault state, and obtain the operation data of other resource devices; for any other resource device, according to the operation data of other resource devices and/or the southbound link connection of other resource devices If the state of other resource devices is a simulated fault state, set the northbound link connection of other resource devices to a simulated fault state; repeat the above steps until the business state is determined, and get Pre-judgment results of whether customers and services are available;

If the status of other resource devices is normal, set the northbound link connection of other resource devices to normal status.

Optionally, the simulation module 430 is further adapted to:

If the operating data of other resource devices is normal and the status of all southbound links of other resource devices is normal, then determine the status of other resource devices as normal;

If the operating data of other resource devices is faulty, or the status of all southbound link connections of other resource devices is a simulated fault state, then it is determined that the status of other resource devices is a simulated fault state.

Optionally, the matching module 440 is further adapted to:

Calculate the matching degree of the pre-judgment result according to the service pre-judgment available status, business pre-judgment unavailable status and unavailable business data contained in the pre-judgment result;

Determine the resource device with the highest matching degree as the faulty resource device; the matching degree includes the accuracy rate of the prediction result, the precision rate of the prediction result and/or the recall rate of the prediction result; the accuracy rate of the prediction result is specifically: the correct prediction rate in the prediction result The proportion of the number of services that are predicted to be available and unavailable to the total number of services; the total number of services includes the number of services that are correctly predicted to be available and unavailable The number of services that are predicted to be available and unavailable; the accuracy rate of the prediction results is as follows: the number of services that are correctly predicted to be unavailable in the prediction results, and the number of services that are predicted to be unavailable in the prediction results The proportion of the number of businesses judged to be unavailable; the recall rate of the prediction results is specifically: the proportion of the number of businesses that are correctly predicted to be unavailable in the prediction results to the unavailable business data.

For the descriptions of the above modules, refer to the corresponding descriptions in the method embodiments, and details are not repeated here.

The present application also provides a non-volatile computer storage medium, the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the model-based fault location method in any method embodiment above.

Fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.

As shown in FIG. 5 , the electronic device may include: a processor (processor) 502 , a communication interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

in:

The processor 502 , the communication interface 504 , and the memory 506 communicate with each other through the communication bus 508 .

The communication interface 504 is configured to communicate with network elements of other devices such as clients or other servers.

The processor 502 is configured to execute the program 510, specifically, may execute relevant steps in the above embodiment of the model-based fault location method.

Specifically, the program 510 may include program codes including computer operation instructions.

The processor 502 may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the electronic device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.

The memory 506 is used for storing the program 510 . The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 510 may be specifically configured to enable the processor 502 to execute the model-based fault location method in any of the foregoing method embodiments. For the specific implementation of each step in the program 510, reference may be made to the corresponding description of the corresponding steps and units in the above-mentioned embodiment of model-based fault location, and details are not repeated here. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the foregoing method embodiments, and details are not repeated here.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other device. Various generic systems can also be used with the teachings based on this. The structure required to construct such a system is apparent from the above description. Furthermore, the present invention is not specific to any particular programming language. It should be understood that various programming languages can be used to implement the contents of the present invention described herein, and the above description of specific languages is for disclosing the best mode of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, in order to streamline this disclosure and to facilitate an understanding of one or more of the various inventive aspects, various features of the invention are sometimes grouped together in a single embodiment, figure, or its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art can understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will understand that although some embodiments described herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention. and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in the model-based fault location device according to the embodiment of the present invention. The present invention can also be implemented as an apparatus or an apparatus program (for example, a computer program and a computer program product) for performing a part or all of the methods described herein. Such a program for realizing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

Claims (9)

1. A method for model-based fault location, the method comprising:

constructing a client-service-resource equipment model; the client-service-resource equipment model records the association topological relation among clients, services and resource equipment;

receiving a declaration request which is triggered by a client and carries unavailable service data;

traversing the resource equipment in the client-service-resource equipment model, and simulating to obtain a prejudgment result of whether the resource equipment is available for the client and the service in a simulated fault state aiming at any resource equipment;

calculating the matching degree according to the pre-judgment result and the unavailable service data, and positioning the resource equipment with the fault according to the calculated matching degree;

the step of traversing the resource device in the client-service-resource device model, for any resource device, obtaining a pre-determination result of whether the resource device is available for the client and the service in a simulated fault state by simulation further includes:

traversing the resource equipment in the client-service-resource equipment model, and setting the resource equipment to be in a simulated fault state aiming at any resource equipment in a simulated mode;

setting each link connection in the northbound direction of the resource equipment in the client-service-resource equipment model as a simulated fault state;

and sequentially setting the states of other resource equipment in the northward direction of each link connecting line simulating the fault state, and obtaining a prejudgment result whether the client and the service are available according to the states of the other resource equipment.

2. The method of claim 1, wherein the building a client-service-resource device model further comprises:

scanning to obtain resource equipment information of an IDC internet data center network; the resource equipment information comprises a resource equipment type, a north-south link relation and a network type; the resource equipment comprises port equipment, access equipment, convergence equipment and routing equipment;

constructing a resource equipment operation and maintenance model according to the resource equipment information; the resource equipment operation and maintenance model comprises port equipment, access equipment, convergence equipment, routing equipment and south-north multi-level link connection among the resource equipment;

acquiring client information, service information, port resource equipment information and north-south flow information of the IDC network, and constructing a service operation model; the service operation model comprises client information, service information, port resource equipment information and a south-north multi-level link connection line;

and combining the same port resource equipment between the resource equipment operation and maintenance model and the service operation model to obtain a client-service-resource equipment model.

3. The method of claim 1, further comprising:

determining associated resource equipment corresponding to unavailable services according to the reporting request and the client-service-resource equipment model;

detecting whether the associated resource equipment is in a fault state;

and if so, repairing the associated resource equipment.

4. The method of claim 1, wherein the sequentially setting the states of other resource devices in the northbound direction of each link connection line simulating the fault state, and obtaining the prediction result of whether the customer and the service are available according to the states of the other resource devices further comprises:

extracting other resource equipment in the northward direction of each link connecting line simulating the fault state, and acquiring the operating data of the other resource equipment; for any other resource equipment, determining the states of the other resource equipment according to the operating data of the other resource equipment and/or the state of the southbound link connection of the other resource equipment; if the states of other resource equipment are simulated fault states, setting the northbound link connection lines of the other resource equipment as simulated fault states; circularly executing the steps until the state of the service is determined, and obtaining a prejudgment result whether the client and the service are available;

and if the state of the other resource equipment is the normal state, setting the northbound link connection line of the other resource equipment to be the normal state.

5. The method of claim 4, wherein determining the state of the other resource device according to the operating data of the other resource device and/or the state of the southbound link connection of the other resource device for any other resource device further comprises:

if the operating data of other resource equipment is normal and the states of all southbound link connecting lines of other resource equipment are normal, determining that the states of other resource equipment are normal;

and if the running data of other resource equipment fails or the states of all southbound link connecting lines of other resource equipment are the simulated fault states, determining that the states of other resource equipment are the simulated fault states.

6. The method according to claim 1, wherein the calculating a matching degree according to the pre-determined result and the unavailable service data, and locating the resource device having the fault according to the calculated matching degree further comprises:

calculating the matching degree of the pre-judging result according to the service pre-judging available state, the service pre-judging unavailable state and the unavailable service data contained in the pre-judging result;

determining the resource equipment with the highest matching degree as the failed resource equipment; the matching degree comprises the accuracy rate of the pre-judgment result, the accuracy rate of the pre-judgment result and/or the recall rate of the pre-judgment result; the accuracy of the pre-judgment result is specifically as follows: the proportion of the service number of the service pre-judging available state and the service pre-judging unavailable state in the pre-judging result in the total service number is correctly pre-judged; the total number of services comprises the number of services in a state that the service is available for correct pre-judgment and in a state that the service is unavailable for pre-judgment, and the number of services in a state that the service is available for error pre-judgment and in a state that the service is unavailable for pre-judgment; the accurate rate of the pre-judgment result is specifically as follows: the ratio of the number of services in the service pre-judging unavailable state in the pre-judging result to the number of services in the service pre-judging unavailable state in the pre-judging result is pre-judged; the recall rate of the pre-judgment result is specifically as follows: and the proportion of the number of services in the unavailable state of correct pre-judgment service pre-judgment in the pre-judgment result in the unavailable service data.

7. A model-based fault location device, the device comprising:

a construction module adapted to construct a client-service-resource device model; the client-service-resource equipment model records the association topological relation among clients, services and resource equipment;

the receiving module is suitable for receiving a declaration request which is triggered by a client and carries unavailable service data;

the simulation module is suitable for traversing the resource equipment in the client-service-resource equipment model and simulating to obtain a prejudgment result whether the resource equipment is available for the client and the service in a simulated fault state aiming at any resource equipment;

the matching module is suitable for calculating the matching degree according to the pre-judgment result and the unavailable service data and positioning the resource equipment with the fault according to the calculated matching degree;

the simulation module is further adapted to:

traversing the resource equipment in the client-service-resource equipment model, and setting the resource equipment to be in a simulated fault state aiming at any resource equipment in a simulated mode;

setting each link connection in the northbound direction of the resource equipment in the client-service-resource equipment model as a simulated fault state;

and sequentially setting the states of other resource equipment in the northward direction of each link connecting line simulating the fault state, and obtaining a prejudgment result whether the client and the service are available according to the states of the other resource equipment.

8. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus;

the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the model-based fault localization method of any of claims 1-6.

9. A computer storage medium having stored therein at least one executable instruction that causes a processor to perform operations corresponding to the model-based fault localization method of any one of claims 1-6.

Images