WO2015117469A1

WO2015117469A1 - Method, apparatus and system for learning device capability

Info

Publication number: WO2015117469A1
Application number: PCT/CN2014/092062
Authority: WO
Inventors: 吴水生; 齐进
Original assignee: 中兴通讯股份有限公司
Priority date: 2014-08-19
Filing date: 2014-11-24
Publication date: 2015-08-13
Also published as: CN105407112A; CN105407112B; MY192748A

Abstract

Disclosed is a method for learning a device capability. The method comprises: after establishing a connection with a device, respectively acquiring, by a capability learner, parameter model data and behaviour data of the device; while acquiring the two categories of data, recording abnormal data generated when acquiring the two categories of data; and conducting persistence on the acquired parameter model data and behaviour data and the abnormal data. Also disclosed at the same time are a system and apparatus for achieving the method.

Description

Method, device and system for learning device capability

Technical field

The present invention relates to a device capability simulation technology in the field of communications, and in particular, to a device capability learning method, apparatus, and system.

Background technique

The telecom network management system is responsible for managing various types of equipment. The network management and equipment need 24 hours of uninterrupted data exchange and interactive response. These exchanges and interactions will put pressure on the network management system. Therefore, in order to ensure system stability and performance, it is necessary to stress test the network management system in advance to cope with the pressure situation on site. In addition, in the current network environment, the types of devices are complex. In many cases, the network management systems and devices used by operators are not affiliated with the same vendor. This may result in network management systems and devices from different vendors needing to be connected to the network. The docking test is carried out, and the test can be applied before it can be formally applied.

For the test scenarios of the above two network management systems, a large number of devices are required to support the construction of the test environment. However, in general, it is almost impossible for a laboratory environment to have enough real equipment. Therefore, in order to achieve the test purpose, many times, the device simulator is used to simulate the real device, which is used to simulate the above test scenario, or simulate specific device behavior, and test the fault tolerance and compatibility of the network management system to improve the test coverage. purpose.

In order to simulate different data or different behaviors, the existing simulator generally requires a large amount of manual configuration work, and finally the simulation of a certain type of device can be completed. This method is simple and easy to understand, but it has the following limitations: In the field environment, the number of devices is large, often there are hundreds of thousands. If you want to completely simulate the on-site environment, it takes a lot of manpower and resources, and can not The change of the device automatically adapts. Therefore, in reality, only some typical devices can be selected as test objects, and the field environment cannot be completely simulated.

Summary of the invention

In order to solve the existing technical problems, embodiments of the present invention provide a method, an apparatus, and a system for learning device capabilities.

An embodiment of the present invention provides a method for learning device capabilities, where the method includes:

After the capability learner establishes a connection with the device, the parameter model data and the behavior data of the device are separately acquired; while the two types of data are acquired, the abnormal data that occurs when the two types of data are acquired are recorded; The parametric model data, the behavior data, and the abnormal data are persisted.

In the above solution, the parameter model data includes one or more of the following: a parameter model, a parameter value, and a parameter type.

In the above solution, the behavior data includes one or two of the following: the behavior data of the network management device actively triggering the device, and the behavior data triggered by the device.

In the above solution, the method further includes: the capability learner pre-storing the relevant standard specifications followed by the device to be learned, and/or the standard protocol, and/or the agreement of the private protocol.

In the above solution, the obtaining the parameter model data and the behavior data of the device includes:

Recursively traversing the parameter tree of the device from the root node to obtain parameter model data of the device; testing the data when the device performs the behavior according to the behavior list of the device, and recording corresponding data; wherein the behavior list is located in the correlation Standard specifications, and/or standard protocols, and/or proprietary protocols.

After the capability learner establishes the connection with the device, the parameter model data and the behavior data of the device are respectively acquired; the capability learner records the abnormal data that occurs when the two types of data are acquired while acquiring the two types of data; The learner persists the acquired parameter model data, behavior data, and the abnormal data.

In the above solution, the method further includes:

When performing device capability simulation, the simulator loads the persistent data in the capability learner and performs simulation of the device.

An embodiment of the present invention provides a learning device for a device, where the learning device is the capability learner, and includes: an obtaining module, a recording module, and a persistence module;

The acquiring module is configured to acquire parameter model data and behavior data of the device after the capability learner establishes a connection with the device;

The recording module is configured to record, when the acquiring module acquires the two types of data, abnormal data that occurs when the two types of data are acquired;

The persistence module is configured to persist the parameter model data and behavior data acquired by the obtaining module and the abnormal data recorded by the recording module.

In the above solution, the capability learner further includes: a storage module configured to pre-store the relevant standard specifications, and/or standard protocols, and/or proprietary protocols of the device to be learned.

Embodiments of the present invention provide a device capability learning system, where the system includes the capability learner described above.

In the above solution, the system further includes: a simulator, including: a loading module and an analog module; wherein

The loading module is configured to load persistent data in the capability learner when performing device capability simulation;

The simulation module is configured to perform simulation of the device according to the persistent data loaded by the loading module.

The device learning method, device and system provided by the embodiment of the present invention, after the capability learner establishes a connection with the device, respectively acquires parameter model data and behavior data of the device; and the capability learner acquires the two types of data simultaneously Obtaining abnormal data that occurs when the two types of data are acquired; the capability learner persists the acquired parameter model data, behavior data, and the abnormal data. The embodiment of the invention can learn the parameter set and behavior of different types of devices, and The simulator provides data sources without human intervention, without the need to manually configure various parameters, greatly reducing the configuration workload of the simulator, especially in setting up specific test scenarios, restoring the field environment and large-scale stress test scenarios. effective.

DRAWINGS

In the drawings, which are not necessarily to scale, the Like reference numerals with different letter suffixes may indicate different examples of similar components. The drawings generally illustrate the various embodiments discussed herein by way of example and not limitation.

1 is a schematic flowchart of an implementation process of a device capability learning method according to an embodiment of the present invention;

2 is a schematic flowchart of a method for acquiring parameter model data of a device according to an embodiment of the present invention;

3 is a schematic flowchart of a method for acquiring behavior data of a device according to an embodiment of the present invention;

4 is a schematic structural diagram of a device for learning a device capability according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a learning system for device capabilities according to an embodiment of the present invention.

detailed description

In the embodiment of the present invention, after the capability learner establishes a connection with the device, the parameter model data and the behavior data of the device are respectively acquired; and the capability learner records the two types of data while acquiring the two types of data. The abnormal data that appears; the capability learner persists the obtained parameter model data, the behavior data, and the abnormal data.

The capability learner is a device corresponding to the device capability learning method in the embodiment of the present invention.

In the embodiment of the present invention, the device capability is mainly represented by two types of data: parameter model data and behavior data.

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

FIG. 1 is a schematic flowchart of an implementation process of a device capability learning method according to an embodiment of the present invention, such as As shown in Figure 1, it includes:

Step 100: After establishing a connection between the capability learner and the device, acquiring parameter model data and behavior data of the device respectively.

In the embodiment of the present invention, the capability learner is connected to the device to be learned, and after the interaction channel is established, the capability of the learning device is ready to start learning. After the capability learner receives the request of the device, the learning process starts, and of course, the capability learner can also take the initiative. Initiate a learning process to the device. The capability learner first obtains the parameter model data of the device, for example, recursively traverses the parameter tree of the device from the root node to obtain parameter model data of the device; the parameter model data may be: parameter model, parameter value, and parameter type. One or more.

After the parameter model data is acquired, the capability learner sequentially tests the data when the device performs the behavior according to the behavior list of the device, and records the corresponding data, and the content of the data includes not limited to the interactive message, the device processing delay, and the device abnormal performance. Wait. Wherein the list of behaviors is located in the relevant standard specification, and/or standard protocol, and/or proprietary protocol.

Of course, in the embodiment of the present invention, the order of the parameter model data and the acquisition behavior data of the acquiring device is not strictly limited.

In the embodiment of the present invention, the reason for acquiring the parameter model data of the device in the device capability learning process is: since the various behaviors and data of the device are determined by the parameter model of the device and the corresponding data, the parameter model of the device is the basis. . In addition, devices generally follow the conventions of standard specifications, protocols, or proprietary protocols. With these protocols, the capability learner can attempt to traverse the parameter model of the device and obtain information such as parameter models, parameter values, and parameter types during the traversal process.

The reason for obtaining the behavior data of the device during the device capability learning process is that the device behavior to be processed in the network management system management device includes one or two of the following: one is the behavior of the network management device actively triggering the device, and the other is the device. Behavior that is triggered under certain conditions; among them,

The behavior of the network controller actively triggers: This scenario can trigger the corresponding behavior by simulating the network management packet, so as to record the entire process of interaction between the device and the network management system;

Behavior triggered by the device: This scenario is triggered by setting device parameters or manually operating the device.

After the corresponding behavior of the triggering device, the complete message exchanged between the device and the network management device is recorded, and the behavior within the protocol specification and the behavior specific to the device are analyzed.

Taking the Tr069 protocol as an example, the unique behavior of the device includes the event number of the Inform, the delay between each step in the interaction process, and the different responses of the device under various abnormal conditions.

Step 102: The capability learner records the abnormal data that occurs when the two types of data are acquired while acquiring the two types of data.

In the embodiment of the present invention, when the capability learner acquires the parameter model data and the behavior data, an exception occurs, and the capability learner acquires various abnormalities occurring in the two types of data acquisition processes, and records the abnormal data. For example, the abnormal data may be: a parameter value of the path obtained for a parameter path that does not exist; or a parameter value that does not meet the parameter type is sent, for example, the parameter type is an integer, but one is issued. String, can also be other exceptions, etc.

Step 104: The capability learner persists the acquired parameter model data, behavior data, and the abnormal data.

In the embodiment of the present invention, the capability learner stores the acquired parameter model data, behavior data, and the abnormal data as a required data type, for example, the data may be stored in an Extensible Markup Language (XML) file. In the implementation of data persistence. Of course, the way to persist is not limited, the most common way is to store as a file.

The persistence of the data, that is, the storage of data, such as objects in memory, to a permanently stored storage device, such as a disk. The main application of persistence is to store in-memory objects in a relational database, of course, in disk files, XML data files, and so on.

In an embodiment of the present invention, if the device capability changes, that is, when the update occurs, the learning method described in steps 100-104 of the embodiment of the present invention is re-executed.

It should be noted that, in the embodiment of the present invention, the capability learner understands that the device to be learned complies with Relevant standard specifications, and/or standard protocols, and/or proprietary protocols, that is, pre-stored relevant standard specifications, and/or standard protocols, and/or in the capability learner. Or the agreement of a proprietary agreement.

In the embodiment of the present invention, the parameter set and behavior of different types of devices can be learned, and the data source is provided for the simulator, and no human intervention is required, and various parameters are not required to be manually configured, thereby greatly reducing the configuration workload of the simulator, especially It is extremely effective in setting up specific test scenarios, restoring the on-site environment, and large-scale stress test scenarios. Moreover, the embodiment of the present invention can also update the device capability at any time, keep the data synchronized with the device, and finally achieve the purpose of restoring the real environment of the site as much as possible.

2 is a schematic flowchart of a method for acquiring parameter model data of a device according to an embodiment of the present invention, as shown in FIG. 2, including:

Step 200: The device is connected to the capability learner, and the capability learner is ready to start traversing the parameter model of the device;

Step 202: After starting the learning process, the capability learner determines whether the traversal is completed, if not, executing step 204; otherwise, executing step 216;

Step 204: The capability learner determines whether the device supports traversing the parameter tree. If yes, step 206 is performed; otherwise, step 216 is performed;

Step 206: Starting from the root node, the capability learner recursively traverses the parameter tree of the device, and then proceeds to step 208;

In the embodiment of the present invention, if the current node has a child node, after obtaining the parameter information of the node, the child node is added to the traversal queue and traversed in sequence, and the parameter information includes: a parameter value of the node and a parameter attribute; The current node is a leaf node, and acquires and records data such as node parameter values and parameter attributes. Among them, one parameter tree corresponds to one parameter model.

Of course, in one embodiment of the present invention, traversal of the parameter tree may be performed using other traversal algorithms than the recursive traversal algorithm.

Step 208: Determine whether the capability learner has an abnormality in the process of acquiring the parameter model data. If an exception occurs, step 210 is performed; otherwise, step 212 is performed;

Step 210: If the abnormality is a critical abnormality, affecting the entire learning process, step 216 is performed to end the learning process; otherwise, step 212 is performed;

Here, the critical exception mainly refers to an unexpected exception, and the abnormality of the abnormal result packet cannot be obtained.

Step 212: The capability learner analyzes the parameter data of the acquired device, filters out the invalid data, converts the acquired data into a data model, for example, converts to an XML format, and caches it in the memory, and then performs step 214;

Step 214: The data in the cache is stored in the file, and then step 216 is performed;

Step 216: The parameter model data acquisition process of the device ends.

FIG. 3 is a schematic flowchart of a method for acquiring behavior data of a device according to an embodiment of the present invention, as shown in FIG. 3, including:

Step 300: The device is connected to the capability learner, and the capability learner is ready to start traversing the device's behavior list.

Here, the list of behaviors of the device can be obtained from the relevant standard specifications, and/or standard protocols, and/or proprietary protocols.

Step 302: After starting the learning process, the capability learner determines whether the traversal is completed, that is, whether the content in the behavior list is tested. If not, step 304 is performed; otherwise, step 310 is performed;

Step 304: The capability learner attempts to issue a command to the device, and then step 306 is performed;

Here, the triggering of the learning behavior is generally triggered by the ability learner. After receiving the behavior command issued by the capability learner, the device parses the command and executes the command, and returns the result of the command execution to the capability learner.

Step 306: Determine whether the device supports testing according to the response of the device to the behavior command. Behavior, if supported, proceed to step 308, otherwise proceed to step 302;

Step 308: Analyze the response result of the device, collect the relevant behavior data and cache, and then perform step 302;

Step 310: After the content in the device behavior list is tested once, the capability learner converts the cached data into a data model, such as: converted into an XML format, and stored in a file, and then step 312 is performed;

Step 312: The behavior data acquisition process of the device ends.

The embodiment of the invention further provides a method for learning device capabilities, the method comprising:

After the capability learner establishes the connection with the device, the parameter model data and the behavior data of the device are respectively acquired; while the two types of data are acquired, the abnormal data that occurs when the two types of data are acquired are recorded; The parameter model data, behavior data, and the abnormal data are persisted.

In an embodiment of the invention, the method further includes:

The capability learner pre-stores the relevant standard specifications followed by the device to be learned, and/or the standard protocol, and/or the agreement of the proprietary protocol.

In an embodiment of the invention, the method further includes:

When the device capability simulation is performed, the simulator can be used to load the persistent data in the capability learner to simulate the device.

Here, the persistent data of the corresponding different types of devices in the capability learner can be loaded as needed to simulate various types of devices.

The detailed description of the method of this embodiment is similar to the above and will not be described in detail herein.

The embodiment of the present invention further provides a learning device for device capability. As shown in FIG. 4, the learning device is the capability learner described above. The capability learner 40 includes: an obtaining module 400 and a recording module 402. And a persistence module 404; wherein

The obtaining module 400 is configured to acquire a connection between the capability learner and the device, respectively Describe the parameter model data and behavior data of the device;

In the embodiment of the present invention, the capability learner is connected to the device to be learned, and after the interaction channel is established, the capability of the learning device is ready to start learning. After the capability learner receives the request of the device, the learning process starts, and of course, the capability learner can also take the initiative. Initiate a learning process to the device. The obtaining module 400 in the capability learner first acquires parameter model data of the device, for example, recursively traversing the parameter tree of the device from the root node to obtain parameter model data of the device; the parameter model data may be: a parameter model and a parameter. One or more of the values and parameter types.

After the parameter model data is acquired, the obtaining module 400 sequentially tests the data when the device performs the behavior according to the behavior list of the device, and records the corresponding data, where the content of the data includes not limited to the interactive message, the device processing delay, and the device. Abnormal performance, etc. Wherein the list of behaviors is located in the relevant standard specification, and/or standard protocol, and/or proprietary protocol.

Of course, in the embodiment of the present invention, the obtaining module 400 acquires the parameter model data of the device and the sequence of acquiring the behavior data is not strictly limited.

The recording module 402 is configured to record, when the acquiring module 400 acquires the two types of data, abnormal data that occurs when the two types of data are acquired;

In the embodiment of the present invention, when the acquiring module 400 in the capability learner acquires the parameter model data and the behavior data, an abnormality occurs, and the recording module 402 in the capability learner acquires the two types of data acquisition. Various exceptions that occur during the process and record these anomalies.

The persistence module 404 is configured to persist the parameter model data and behavior data acquired by the obtaining module 400 and the abnormal data recorded by the recording module 402.

In the embodiment of the present invention, the persistence module 404 in the capability learner stores the acquired parameter model data, behavior data, and the abnormal data as a required data type, for example, the data may be stored in Data persistence in Extensible Markup Language (XML) files. Of course, the way to persist is not limited, the most common way is to store as a file.

In one embodiment of the present invention, the capability learner 40 further includes a storage module 406 configured to pre-store the relevant standard specifications followed by the device to be learned, and/or the standard protocol, and/or the agreement of the proprietary protocol.

The embodiment of the present invention further provides a learning system for device capabilities. As shown in FIG. 5, the system includes the capability learner 40 described above.

In an embodiment of the present invention, the system further includes: a simulator 42 comprising: a loading module 420 and an analog module 422;

The loading module 420 is configured to load persistent data in the capability learner when performing device capability simulation;

The simulation module 422 is configured to perform simulation of the device according to the persistent data loaded by the loading module 420.

In an embodiment of the present invention, if the device capability changes, that is, when an update occurs, the system re-executes the learning method described in steps 100-104 of the embodiment of the present invention.

Those skilled in the art will appreciate that embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.

The present invention is directed to a method, apparatus (system), and computer program in accordance with an embodiment of the present invention The flow chart and/or block diagram of the product is described. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention.

Claims

A method of learning device capabilities, the method comprising:

After the capability learner establishes a connection with the device, the parameter model data and the behavior data of the device are separately acquired; while the two types of data are acquired, the abnormal data that occurs when the two types of data are acquired are recorded; The parametric model data, the behavior data, and the abnormal data are persisted.
The method of claim 1, wherein the parameter model data comprises one or more of the following: a parameter model, a parameter value, and a parameter type.
The method according to claim 1, wherein the behavior data comprises one or two of the following: the network management actively triggers behavior data of the device, and the device-triggered behavior data.
The method according to claim 1, 2 or 3, wherein the method further comprises: the capability learner pre-storing relevant standard specifications followed by the device to be learned, and/or a standard protocol, and/or a convention of a proprietary protocol .
The method of claim 4, wherein the obtaining parameter model data and behavior data of the device comprises:

Recursively traversing the parameter tree of the device from the root node to obtain parameter model data of the device; testing the data when the device performs the behavior according to the behavior list of the device, and recording corresponding data; wherein the behavior list is located in the correlation Standard specifications, and/or standard protocols, and/or proprietary protocols.
A method of learning device capabilities, the method comprising:

After the capability learner establishes the connection with the device, the parameter model data and the behavior data of the device are respectively acquired; the capability learner records the abnormal data that occurs when the two types of data are acquired while acquiring the two types of data; The learner persists the acquired parameter model data, behavior data, and the abnormal data.
The method of claim 6 wherein the method further comprises:

When performing device capability simulation, the simulator loads the persistent data in the capability learner and performs simulation of the device.
A device capable learning device, the learning device according to any one of claims 1 to 5, comprising: an acquisition module, a recording module, and a persistence module;

The acquiring module is configured to acquire parameter model data and behavior data of the device after the capability learner establishes a connection with the device;

The recording module is configured to record, when the acquiring module acquires the two types of data, abnormal data that occurs when the two types of data are acquired;

The persistence module is configured to persist the parameter model data and behavior data acquired by the obtaining module and the abnormal data recorded by the recording module.
The learning apparatus according to claim 8, wherein the capability learner further comprises: a storage module configured to pre-store a relevant standard specification followed by the device to be learned, and/or a standard protocol, and/or a protocol of a proprietary protocol .
A device capable learning system, the system comprising the capability learner of claim 8 or 9.
The system of claim 10, wherein the system further comprises: a simulator comprising: a loading module and an analog module; wherein

The loading module is configured to load persistent data in the capability learner when performing device capability simulation;

The simulation module is configured to perform simulation of the device according to the persistent data loaded by the loading module.