CN112269801A - Asset information acquisition method, device, equipment and readable storage medium - Google Patents
Asset information acquisition method, device, equipment and readable storage medium Download PDFInfo
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- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 6
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- 230000003044 adaptive effect Effects 0.000 claims description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/24569—Query processing with adaptation to specific hardware, e.g. adapted for using GPUs or SSDs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/2247—Verification or detection of system hardware configuration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/02—Standardisation; Integration
- H04L41/0213—Standardised network management protocols, e.g. simple network management protocol [SNMP]
Abstract
The invention discloses an asset information acquisition method, which considers that adaptation plug-ins for data communication with a server between certain approximate models can be universal, so that after a model with the highest similarity to target model characteristic information of a target server in a model library is determined, the model can be used as an approximate model of the target server, then the asset information of the target server can be directly acquired by using the adaptation plug-ins of the approximate models, and because the method is applied to a processor, workers do not need to manually acquire the asset information, the asset information can be quickly acquired from a brand new model, and the working efficiency is greatly improved. The invention also discloses an asset information acquisition device, equipment and a computer readable storage medium, which have the same beneficial effects as the asset information acquisition method.
Description
Technical Field
The invention relates to the technical field of server management, in particular to an asset information acquisition method, and also relates to an asset information acquisition device, equipment and a computer readable storage medium.
Background
In order to better manage the servers in the local area network, the server management platform acquires the asset information of the server newly accessed to the local area network, but when the model of the newly accessed server is a brand new model, the asset information of the server cannot be quickly and efficiently acquired in the prior art, even the staff is required to manually acquire the asset information, and the working efficiency is low.
Therefore, how to provide a solution to the above technical problem is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide an asset information acquisition method, which can quickly acquire asset information of a brand new machine type without manually acquiring the asset information by workers, thereby greatly improving the working efficiency; another object of the present invention is to provide an asset information acquisition apparatus, device and computer-readable storage medium, which can quickly acquire asset information from a completely new model without requiring a worker to manually acquire the asset information, thereby greatly improving the work efficiency.
In order to solve the above technical problem, the present invention provides an asset information acquisition method, applied to a processor, comprising:
acquiring target machine type characteristic information of a target server;
determining the similarity between the target machine type characteristic information and machine type characteristic information of various servers prestored in a machine type library by adopting a preset similarity calculation method;
taking the model of the server in the model base corresponding to the highest similarity as an approximate model of the target server;
and acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
Preferably, after the target model feature information of the target server is obtained, before the similarity between the target model feature information and model feature information of each type of server pre-stored in the model base is determined by using a preset similarity algorithm, the asset information obtaining method further includes:
judging whether the model of the target server is an existing model in a model library or not according to the target model characteristic information;
if yes, acquiring an adaptive plug-in corresponding to the actual model of the target server;
acquiring asset information of the target server by using an adaptation plug-in corresponding to the actual model of the target server;
and if not, executing the step of determining the similarity between the target machine type characteristic information and the machine type characteristic information of various servers prestored in the machine type library by adopting a preset similarity algorithm.
Preferably, the obtaining of the target model feature information of the target server specifically includes:
scanning based on a preset type of scanning protocol to obtain target machine type characteristic information of a target server;
the preset type of scanning protocol is specifically any one of a Simple Network Management Protocol (SNMP), an Intelligent Platform Management Interface (IPMI), a storage management plan specification (SMIS) and a secure shell protocol (SSH).
Preferably, the determining the similarity between the target model feature information and the model feature information of each type of server pre-stored in the model library by using a preset similarity algorithm specifically includes:
and taking the ratio of the number of the same parameters to the total number of all the parameters as the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model library.
Preferably, the target model feature information and the model feature information are the same and include a manufacturer, a model and a version.
Preferably, the adaptation plug-in is specifically a data communication interface encapsulating a server of a corresponding model and an adaptation interface encapsulating a data communication protocol.
Preferably, the obtaining of the target model feature information of the target server specifically includes:
responding to a server scanning instruction, scanning a newly accessed server in the local area network and taking the newly accessed server as a target server;
acquiring target machine type characteristic information of a target server;
the server scanning instruction is a server scanning instruction received by a man-machine interaction device or a server scanning instruction generated periodically.
In order to solve the above technical problem, the present invention further provides an asset information acquiring apparatus, applied to a processor, including:
the first acquisition module is used for acquiring the target machine type characteristic information of the target server;
the determining module is used for determining the similarity between the target machine type characteristic information and machine type characteristic information of various servers prestored in a machine type library by adopting a preset similarity calculation method;
the mapping module is used for taking the model of the server in the model base corresponding to the highest similarity as an approximate model of the target server;
and the second acquisition module is used for acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
In order to solve the above technical problem, the present invention further provides an asset information acquiring apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the asset information acquisition method as described in any one of the above when executing the computer program.
To solve the above technical problem, the present invention further provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the steps of the asset information acquisition method according to any one of the above.
The invention provides an asset information acquisition method, which considers that adaptation plug-ins for data communication with a server between certain approximate models can be universal, so that in the method, after a model with the highest similarity to the target model characteristic information of a target server in a model library is determined, the model can be used as the approximate model of the target server, and then the asset information of the target server can be directly acquired by using the adaptation plug-ins of the approximate models.
The invention also provides an asset information acquisition device, equipment and a computer readable storage medium, which have the same beneficial effects as the asset information acquisition method.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the prior art and the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of an asset information acquisition method according to the present invention;
FIG. 2 is a schematic structural diagram of an asset information acquisition device according to the present invention;
fig. 3 is a schematic structural diagram of an asset information acquisition device according to the present invention.
Detailed Description
The core of the invention is to provide an asset information acquisition method, which can quickly acquire asset information from a brand new machine type without the need of manually acquiring the asset information by workers, thereby greatly improving the working efficiency; another core of the present invention is to provide an asset information acquisition apparatus, device and computer-readable storage medium, which can quickly acquire asset information from a brand new model without requiring a worker to manually acquire the asset information, thereby greatly improving the work efficiency.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of an asset information acquiring method provided by the present invention, where the asset information acquiring method includes:
step S1: acquiring target machine type characteristic information of a target server;
specifically, in view of the technical problems in the background art as described above, and in view of the fact that the adaptation plug-ins for data communication with the server are common between some approximate models, in the embodiment of the present invention, it may be attempted to acquire asset information of the target server by using the adaptation plug-ins of models similar to the target server, and in order to acquire a model similar to the target server, it is first necessary to analyze model feature information of the target server, so in the embodiment of the present invention, target model feature information of the target server may be first acquired so as to be matched with the approximate model of the target server in a subsequent step.
The target server in the embodiment of the present invention may refer to a server of an unknown model, and may be of various types, which is not limited herein.
Specifically, the model feature information may be used to characterize the model feature of the target server, and the model feature information has a close relationship with the adaptation plug-in, for example, all servers of a certain manufacturer may all use the same adaptation plug-in to obtain asset information, and the like.
Step S2: determining the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model library by adopting a preset similarity calculation method;
specifically, model characteristic information of various servers of the existing models is prestored in the model base, and in order to accurately find the approximate model of the target server, in the embodiment of the invention, a preset similarity calculation method can be used for determining the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model base, so that the similarity can be compared and the approximate model of the target server can be found.
Step S3: taking the model of the server in the model base corresponding to the highest similarity as the approximate model of the target server;
specifically, the model of the server in the model library corresponding to the highest similarity has the highest feature matching degree with the model of the target server, so that the probability that the corresponding adaptation plug-in can be applied to the target server is also highest, and therefore, in the embodiment of the invention, the model of the server in the model library corresponding to the highest similarity is taken as the approximate model of the target server, and the asset information of the target server can be successfully acquired at high probability.
Certainly, although the probability that the adaptation plug-in corresponding to the model of the server in the model library corresponding to the highest similarity is applicable to the target server is the highest, there is also a possibility that the adaptation plug-in is not applicable, so a preset threshold may be set, the similarity with the numerical value higher than the preset threshold is firstly reserved, and then the worker analyzes and judges the approximate model from the reserved similarity by additionally combining with manual experience, so as to obtain asset information of the target server according to the adaptation plug-in of the approximate model in the following step.
Step S4: and acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
Specifically, the asset information of the target server can be obtained by using the determined adaptation plug-in of the approximate model, the asset information of the target server can be successfully obtained with a high probability, and the embodiment of the invention has a high execution speed and greatly improves the working efficiency because the embodiment of the invention is applied to the processor.
The invention provides an asset information acquisition method, which considers that adaptation plug-ins for data communication with a server between certain approximate models can be universal, so that in the method, after a model with the highest similarity to the target model characteristic information of a target server in a model library is determined, the model can be used as the approximate model of the target server, and then the asset information of the target server can be directly acquired by using the adaptation plug-ins of the approximate models.
On the basis of the above-described embodiment:
as a preferred embodiment, after obtaining the target model feature information of the target server, before determining the similarity between the target model feature information and the model feature information of each type of server pre-stored in the model library by using a preset similarity calculation method, the asset information obtaining method further includes:
judging whether the model of the target server is an existing model in the model library or not according to the characteristic information of the target model;
if yes, acquiring an adaptive plug-in corresponding to the actual model of the target server;
acquiring asset information of a target server by using an adaptation plug-in corresponding to an actual model of the target server;
and if not, determining the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model base by adopting a preset similarity calculation method.
Specifically, in order to enable the method in the embodiment of the present invention to be applicable to all target servers newly accessing to the local area network, that is, not only to acquire asset information from a target server of a brand new unknown model, the embodiment of the present invention may determine whether the model of the target server is an existing model in the model library according to the characteristic information of the target model, if the model is an existing model, the matter becomes simple, and only the asset information of the target server needs to be acquired according to an adaptive model corresponding to the actual model of the target server, thereby further improving the working efficiency.
Specifically, the process of determining whether the model of the target server is an existing model in the model base according to the target model feature information may also be to compare the model feature information, and if the model is an existing model, a model with a similarity of 100% to the target model feature information is inevitably present in the model base.
As a preferred embodiment, the obtaining of the target model feature information of the target server specifically includes:
scanning based on a preset type of scanning protocol to obtain target machine type characteristic information of a target server;
the preset type of scanning Protocol is specifically any one of SNMP (Simple Network Management Protocol), IPMI (Intelligent Platform Management Interface), SMIS (SMI Specification, storage Management plan Specification) and SSH (Secure Shell Protocol).
Specifically, in order to smoothly scan and obtain the target model feature information of the target server, a scanning protocol is required to scan the target feature information, and the above protocols all have the advantages of strong stability, high processing speed and the like.
Of course, besides the above various protocols, other various types of scanning protocols may be adopted to scan the target model feature information of the target server, and the embodiment of the present invention is not limited herein.
As a preferred embodiment, the determining the similarity between the target model feature information and the model feature information of each type of server pre-stored in the model library by using a preset similarity algorithm specifically includes:
and taking the ratio of the number of the same parameters to the total number of all the parameters as the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model library.
Specifically, because the model feature information includes a plurality of parameters, the ratio of the number of the same parameters to the total number of all the parameters can be used as the similarity between the target model feature information and the model feature information of each type of server pre-stored in the model library, and the similarity can be calculated relatively easily and quickly, for example, the model feature information includes three ABC parameters in total, and if the AB parameters of the model feature information of a certain model and the AB parameters of the target model feature information are the same and the C parameters are different, the similarity between the model feature information of the certain model and the target model feature information is 2/3.
In consideration of the fact that the association degrees of different parameters with the adaptation plug-in are different, different weights may be assigned to the different parameters, and taking the three ABC parameters as an example, 50% of the weight may be assigned to the parameter a, and 25% of the weight may be assigned to the parameter B and the parameter C, respectively, so that it is assumed that the AB parameters of the model feature information of a certain model and the AB parameters of the target model feature information are the same, and the similarity between the parameter C and the target model feature information is 75%.
Of course, besides the above-listed methods for calculating the similarity, there may be other types of methods for calculating the similarity, and the embodiment of the present invention is not limited herein.
As a preferred embodiment, the target model characteristic information and the model characteristic information are the same and include manufacturer, model and version.
Specifically, manufacturers, models and versions are closely related to models and adapter plug-ins, so in the embodiment of the present invention, manufacturers, models and versions are used as model feature information.
Of course, in addition to the above listed parameters, the target model feature information and the model feature information may be of other specific types, and the embodiment of the present invention is not limited herein.
As a preferred embodiment, the adaptation plug-in is specifically a data communication interface encapsulating a server of a corresponding model and an adaptation interface encapsulating a data communication protocol.
Specifically, the data communication interface and the data communication protocol may facilitate smooth communication with the target server to obtain the asset information thereof, and therefore the adaptation plug-in the embodiment of the present invention is specifically a data communication interface encapsulating a server of a corresponding machine type and an adaptation interface encapsulating a data communication protocol.
Of course, the adapter plug-in may also be of other various types, and the embodiment of the present invention is not limited herein.
As a preferred embodiment, the obtaining of the target model feature information of the target server specifically includes:
responding to a server scanning instruction, scanning a newly accessed server in the local area network and taking the newly accessed server as a target server;
acquiring target machine type characteristic information of a target server;
the server scanning instruction is a server scanning instruction received by the man-machine interaction device or a server scanning instruction generated periodically.
Specifically, a local area network can occasionally access a new server device, in order to manage asset information of a server in the local area network, the server in the local area network needs to be scanned, so that the newly accessed server is scanned and the asset information is acquired, a server scanning instruction can be a periodically automatically generated instruction or an instruction sent by a worker in real time through a human-computer interaction device, the periodically automatically generated instruction and the instruction have different advantages, the workload of the worker can be reduced, and the worker can acquire the asset information of the server newly accessed to the local area network at the first time through the instruction sent by the human-computer interaction device in real time.
Of course, in addition to the above manner, the target model feature information obtained from the target server may be of other specific types, and the embodiment of the present invention is not limited herein.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an asset information acquisition apparatus provided in the present invention, the asset information acquisition apparatus is applied to a processor, and includes:
the first acquisition module 1 is used for acquiring target machine type characteristic information of a target server;
the determining module 2 is used for determining the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model base by adopting a preset similarity calculation method;
the mapping module 3 is used for taking the model of the server in the model base corresponding to the highest similarity as an approximate model of the target server;
and the second acquisition module 4 is used for acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
For introduction of the asset information acquisition device provided in the embodiment of the present invention, reference is made to the foregoing embodiment of the asset information acquisition method, and details of the embodiment of the present invention are not repeated herein.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an asset information acquiring device provided by the present invention, where the asset information acquiring device includes:
a memory 5 for storing a computer program;
a processor 6 for implementing the steps of the asset information acquisition method as in any one of the preceding embodiments when executing the computer program.
For introduction of the asset information acquisition device provided in the embodiment of the present invention, reference is made to the foregoing embodiment of the asset information acquisition method, and details of the embodiment of the present invention are not repeated herein.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the asset information acquisition method as in any one of the preceding embodiments.
For introduction of the computer-readable storage medium provided by the embodiment of the present invention, please refer to the embodiment of the asset information obtaining method, which is not described herein again.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An asset information acquisition method applied to a processor includes:
acquiring target machine type characteristic information of a target server;
determining the similarity between the target machine type characteristic information and machine type characteristic information of various servers prestored in a machine type library by adopting a preset similarity calculation method;
taking the model of the server in the model base corresponding to the highest similarity as an approximate model of the target server;
and acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
2. The asset information acquisition method according to claim 1, wherein after the target model feature information of the target server is acquired, before the similarity between the target model feature information and model feature information of each type of server prestored in the model base is determined by using a preset similarity calculation method, the asset information acquisition method further comprises:
judging whether the model of the target server is an existing model in a model library or not according to the target model characteristic information;
if yes, acquiring an adaptive plug-in corresponding to the actual model of the target server;
acquiring asset information of the target server by using an adaptation plug-in corresponding to the actual model of the target server;
and if not, executing the step of determining the similarity between the target machine type characteristic information and the machine type characteristic information of various servers prestored in the machine type library by adopting a preset similarity algorithm.
3. The asset information acquisition method according to claim 2, wherein the acquiring of the target model feature information of the target server specifically includes:
scanning based on a preset type of scanning protocol to obtain target machine type characteristic information of a target server;
the preset type of scanning protocol is specifically any one of a Simple Network Management Protocol (SNMP), an Intelligent Platform Management Interface (IPMI), a storage management plan specification (SMIS) and a secure shell protocol (SSH).
4. The asset information acquisition method according to claim 2, wherein the determining of the similarity between the target model feature information and the model feature information of each type of server prestored in the model base by using a preset similarity algorithm specifically includes:
and taking the ratio of the number of the same parameters to the total number of all the parameters as the similarity between the target model characteristic information and the model characteristic information of various servers prestored in the model library.
5. The asset information acquisition method according to claim 4, wherein the target model feature information and the model feature information are the same and each includes a manufacturer, a model, and a version.
6. The asset information acquisition method according to claim 1, wherein the adaptation plug-in is specifically a data communication interface encapsulating a server of a corresponding model and an adaptation interface of a data communication protocol.
7. The asset information acquisition method according to any one of claims 1 to 6, wherein the acquiring of the target model feature information of the target server specifically includes:
responding to a server scanning instruction, scanning a newly accessed server in the local area network and taking the newly accessed server as a target server;
acquiring target machine type characteristic information of a target server;
the server scanning instruction is a server scanning instruction received by a man-machine interaction device or a server scanning instruction generated periodically.
8. An asset information acquisition apparatus applied to a processor, comprising:
the first acquisition module is used for acquiring the target machine type characteristic information of the target server;
the determining module is used for determining the similarity between the target machine type characteristic information and machine type characteristic information of various servers prestored in a machine type library by adopting a preset similarity calculation method;
the mapping module is used for taking the model of the server in the model base corresponding to the highest similarity as an approximate model of the target server;
and the second acquisition module is used for acquiring the asset information of the target server by using the adaptation plug-in of the approximate model.
9. An asset information acquisition apparatus characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the asset information acquisition method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, realizes the steps of the asset information acquisition method according to any one of claims 1 to 7.
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