Hardware asset classification method, system, and device, and readable storage medium

A technology of asset classification and hardware, which is applied in the direction of computer parts, instruments, characters and pattern recognition, etc., can solve the problems of insufficient consideration, one-sided grouping results, inability to classify similar hardware assets into the same asset group, etc., and achieve accurate feature description , the effect of accurate classification results

Inactive Publication Date: 2018-12-18
HANGZHOU ANHENG INFORMATION TECH CO LTD
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because the IP address-based classification method cannot classify similar hardware assets across network segments into the same asset group, the consideration is not comprehensive enough, resulting in one-sided actual grouping results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Hardware asset classification method, system, and device, and readable storage medium
  • Hardware asset classification method, system, and device, and readable storage medium
  • Hardware asset classification method, system, and device, and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] The following combination figure 1 , figure 1 It is a flow chart of a hardware asset classification method provided in the embodiment of this application, which specifically includes the following steps:

[0061] S101: Obtain the dynamic attributes and static attributes of each hardware asset in the network;

[0062] This step aims to obtain multiple characteristic information of each hardware asset in the network, specifically dynamic attributes (that will change with time) and static attributes (that will not change with time), wherein the dynamic attributes and static attributes are It can include a variety of specific characteristic parameters. For example, static attributes can include: IP address (static IP is usually used in the enterprise, and the situation of dynamic IP is not considered here), vulnerability information, motherboard firmware version, and at least one of the system activation time. The dynamic attributes may include: the number of alarm inform...

Embodiment 2

[0076] The following combination figure 2 , figure 2 The flow chart of another hardware asset classification method provided by the embodiment of this application is different from the first embodiment. This embodiment also adds a scheme for calculating the similarity value of each asset group, and based on the similarity of each asset group Value pairs are screened whether they meet the preset similarity requirements, in order to further improve the similarity of each hardware asset in the asset group. The specific steps are as follows:

[0077] S201: Obtain dynamic attributes and static attributes of each hardware asset in the network;

[0078] S202: Calculate the comprehensive characteristic parameters of each hardware asset according to the dynamic attributes and static attributes of each hardware asset;

[0079] S203: Classify each comprehensive feature parameter to obtain a preset number of asset groups;

[0080] S204: Calculate the similarity value of each asset gr...

Embodiment 3

[0085] The following combination image 3 , image 3 The flow chart of another hardware asset classification method provided by the embodiment of this application is different from the first embodiment. This embodiment sets corresponding weighted values ​​for different types of feature information according to the degree of influence, in order to combine the weighted The weights are calculated according to the weighted method to obtain a comprehensive feature parameter with more accurate feature description, and the K-Means clustering algorithm is used for classification, and based on the K-Means clustering algorithm, it provides a method for adding new hardware assets on a small scale. The way to add it to the appropriate asset group includes the following steps:

[0086] S301: Obtain dynamic attributes and static attributes of each hardware asset in the network;

[0087]S302: Obtain a similarity parameter for each attribute representing the similarity degree between each h...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

A hardware asset classification method is provided. The multiple pieces of attribute information obtained from each hardware asset are calculated synthetically to obtain a more comprehensive and accurate integrated feature parameter capable of describing the features of the hardware asset. Then, only the classification based on various integrated feature parameters by a classification algorithm isneeded. Finally, a certain number of asset groups can be obtained, that is, each asset group contains different hardware assets which fall into the asset group because of their consistent performanceon the comprehensive characteristic parameters. This method uses more attribute information to synthetically describe the characteristics of each hardware asset, which is more comprehensive and moreaccurate, and can make the classification results more accurate. At the same time, the application also disclose a hardware asset classification system and device and a computer-readable storage medium, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the technical field of asset classification, in particular to a hardware asset classification method, system, device and computer-readable storage medium. Background technique [0002] In today's Internet era, the various convenient services based on the network and data that people enjoy are all supported by the lowest-level hardware devices. Regardless of the size of the enterprise, they will have a certain number of hardware devices. These hardware devices may be used to realize the external provision of Services, or used to improve enterprise content office efficiency, as part of enterprise value, these hardware devices are also called enterprise hardware assets. [0003] In reality, all hardware assets in an enterprise are usually purchased in batches of standard versions or customized versions, which means that all hardware assets in the same batch have less difference and less similarity. High, and small diffe...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/241
Inventor 莫凡范渊刘博
Owner HANGZHOU ANHENG INFORMATION TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap