CN110598062A - Importance quantification method and device for data assets and electronic equipment - Google Patents
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Abstract
The embodiment of the specification provides a method and a device for quantifying importance of data assets and electronic equipment. The importance quantification method comprises the following steps: and acquiring link log information of at least two data nodes of the target object. And constructing a link relation vector graph of the at least two data nodes based on the link log information. And determining the weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram. And quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for quantifying importance of data assets, and an electronic device.
Background
At present, more and more enterprises are built through informatization, and the operation and management capabilities are improved. The information-based construction cannot be separated from the data infrastructure, and the importance of each data infrastructure is greatly different. For example, the importance of the service server is greater than that of the AP hotspot in a certain office area. For enterprises (especially for medium and large enterprises), the importance of the data infrastructure is difficult to identify, so that reasonable data early warning strategies and data emergency strategies cannot be formulated.
In this context, it is necessary to provide a technical solution capable of quantifying the importance of data assets.
Disclosure of Invention
An embodiment of the specification aims to provide a method and a device for quantifying importance of data assets and electronic equipment, which can quantify the importance of the data assets, so as to provide technical support for data risk early warning and data emergency processing.
In order to achieve the above object, the embodiments of the present specification are implemented as follows:
in a first aspect, a method for quantifying importance of data assets is provided, including:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
In a second aspect, an apparatus for quantifying importance of data assets is provided, including:
the collection module is used for acquiring the link log information of at least two data nodes of the target object;
the composition module is used for constructing a link relation vector graph of the at least two data nodes based on the link log information;
the calculation module is used for determining the weight corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and the quantization module is used for performing quantization evaluation on the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
In a third aspect, an electronic device is provided that includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
In a fourth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
According to the scheme of the embodiment of the specification, a link relation vector diagram among the data nodes is generated according to the link log information of the data nodes of the target object, and the importance degree of the data nodes is quantitatively evaluated according to the link relation shown in the link relation vector diagram, so that technical support is provided for data risk early warning and data emergency processing. The whole scheme can be mechanically executed based on a computer program, so that the method has higher processing efficiency and accuracy, and is particularly suitable for dynamically mining the data assets of medium-sized and large-sized enterprises.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative efforts.
Fig. 1 is a first flowchart of an importance quantifying method provided in an embodiment of the present disclosure.
Fig. 2 is a link relation vector diagram illustrating an importance quantification method.
Fig. 3 is a second flowchart of an importance quantifying method provided in an embodiment of the present disclosure.
Fig. 4 is a schematic structural diagram of an importance quantization apparatus provided in an embodiment of the present specification.
Fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of this specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
As described above, more and more enterprises are built by informatization to improve operation and management capabilities. The information-based construction cannot be separated from the data infrastructure, and the importance of each data infrastructure is greatly different. For example, the importance of the service server is greater than that of the AP hotspot in a certain office area. For enterprises, it is difficult to identify the importance of these data infrastructures, so that reasonable data early warning strategies and data emergency strategies cannot be formulated.
In view of the above problems, this document aims to provide a technical solution for quantifying importance of data assets, so as to provide information support for risk early warning and emergency treatment of data infrastructure.
FIG. 1 illustrates a method for quantifying importance of data assets according to an embodiment of the present disclosure. The method shown in fig. 1 may be performed by a corresponding apparatus, comprising:
step S102, link log information of at least two data nodes of the target object is obtained.
The target object may be an enterprise, a school, a hospital, or other units, the data node is a data infrastructure in the target object, and may be a router, a gateway, a switch, a server, or the like, and the target object and the data node are not specifically limited in this embodiment of the present specification.
The link log information describes historical link information among the data nodes, and the data network structure of the target object can be determined through the link log information of each data node, so that the historical link quantity of each data node is obtained.
And step S104, constructing a link relation vector graph of at least two data nodes based on the link log information.
In each link relation vector of the link relation vector graph, the data node which actively initiates the link is used as the starting point of the link relation vector, and the data node which initiates the link is used as the ending point of the link relation vector. That is, a link relation vector is formed between two data nodes having a link relation, and the direction of the link relation vector is that the data node which actively initiates the link points to the data node which is initiated to be linked.
For ease of understanding, reference may be made to the link relationship vector graph illustrated in FIG. 2, which is shown in FIG. 2 as having three data nodes, namely data node A, B, C. As can be seen by the direction of the vector, data node a is actively initiating a link to data node B, C and data node B is actively initiating a link to data node C, respectively.
And step S106, determining the weight corresponding to the data node of the target object according to the link relation shown in the link relation vector diagram.
In this step, specifically, based on the link analysis algorithm, the weight of each data node may be calculated according to the total number of each data node shown in the link relation vector diagram as the starting point of the link relation vector and the total number of each data node as the ending point of the link relation vector.
Obviously, the greater the total number of a certain data node as the starting point of the link relation vector and the total number as the ending point of the link relation vector, the greater the importance in the data frame of the target object, and therefore the greater the calculated weight.
It should be understood that the link analysis algorithm is not exclusive and the embodiments of the present specification are not particularly limited thereto.
And S108, quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
It should be noted that the quantization method is not exclusive, and the embodiments of the present specification are not particularly limited thereto. In this step, if the weight is used as the only factor for quantizing the importance of the data node, the weight is the importance.
Based on the importance quantification method shown in fig. 1, it can be known that, in the solution in the embodiment of the present specification, a link relationship vector diagram between data nodes is generated according to link log information of the data nodes of a target object, and the importance of the data nodes is quantitatively evaluated according to the link relationship shown in the link relationship vector diagram, so as to provide technical support for data risk early warning and data emergency processing. The whole scheme can be mechanically executed based on a computer program, so that the method has higher processing efficiency and accuracy, and is particularly suitable for dynamically mining the data assets of medium-sized and large-sized enterprises.
The method of the embodiments of the present specification will be described in detail below.
The method of the embodiment of the specification determines the link relation among the data nodes based on the link log information, and quantifies the importance of the data nodes according to the link relation, thereby achieving the effects of mining data resources and identifying the importance of data assets.
As shown in fig. 3, the main process of the method includes:
in step S301, link log information of each data node of the target object is acquired.
In this embodiment, an Application Programming Interface (API) may be configured in advance for each data node. This step actively calls link log information to each data node based on these API interfaces.
After the link log information is acquired, local associated storage can be performed on the data nodes and the corresponding link log information to serve as a basis.
Step S302, based on the link log information, a link relation network of the data nodes is constructed, and the link relation network is converted into a link relation vector diagram.
Specifically, in this step, the data node that actively initiates the link and the data node that is initiated the link may be determined first through the link log information. And then, taking the data node which actively initiates the link in the same link as a starting point and the data node which initiates the link as an end point to construct a link relation vector so as to obtain a link relation vector diagram.
It should be understood that the topology of the link network formed between the data nodes can be intuitively determined based on the link relation vector shown in the link relation vector diagram.
Step S303, based on the link analysis algorithm, calculating the weight of each data node according to the link relation shown in the link relation vector diagram.
The link analysis algorithm is not unique, and may include, but is not limited to, a Hyperlink-Induced Topic Search (HITS) algorithm, a web Rank (Page Rank) algorithm, and the like.
By way of exemplary introduction, assuming that the Page Rank algorithm is adopted in this step, the corresponding weight value may be quantized according to the number of the data nodes as vector start points and the number of the data nodes as vector end points. That is, the weight of a data node depends on its number as the start of the vector and its number as the end of the vector.
Alternatively, the historical link number corresponding to each vector may be retrieved from the link log information, and the weight value of each vector may be quantized based on the historical link number of each vector. And then, further quantizing the weight values of the data nodes based on the weight values of all vectors associated with the data nodes. For example, a certain data node is used as one point in vector 1, vector 2, and vector 3, and it is determined through the link log information that the historical link number of vector 1 is 10 ten thousand (that is, the link represented by vector 1 is established 10 ten thousand), the historical link number of vector 2 is 20 ten thousand, and the historical link number of vector 3 is 30 ten thousand, then the weight of the data node is: 10aq +20bw +30ce, wherein a is a coefficient for calculating the weight of the vector 1, and 10a represents that the vector 1 corresponds to the weight of 10 ten thousand times; b is a coefficient for calculating the weight of the vector 2, and similarly 20b represents that the vector 2 corresponds to the weight of 20 ten thousand times; c is a coefficient for calculating the weight of the vector 3, and similarly, 30c represents that the vector 3 corresponds to 30 ten thousand times of weights.
Step S304, the importance value of each data node is obtained by quantification according to the weight value of each data node.
The quantization method is not exclusive, and the embodiments of the present specification are not particularly limited. It should be understood that the weight of the data node is positively correlated with the importance value, that is, the greater the weight of the data node is, the greater the corresponding importance value is; conversely, the smaller the weight of the data node, the smaller the corresponding importance value.
In addition, on the basis of the above, the step may also be combined with other information of the data node to quantify the importance value of the data node. That is, the importance value of the data node does not depend only on the weight calculated in step S303. For example, the importance value of the data node may be quantized according to the number of service groups of the data node, that is, the greater the number of service groups of the data node, the greater the corresponding importance value; conversely, the smaller the number of service groups of the data node, the smaller the corresponding importance value.
And S305, performing data asset entry on each data node and the corresponding importance value.
It should be understood that data asset entry may be performed dynamically. For example, when a new data node is mined, the new data node and the corresponding importance value are entered into the database as data asset information. Or when the link topology among the data nodes changes, recalculating the importance value of each data node and synchronizing the data nodes to the database.
In addition, this step may also be performed only for the entry of valuable data assets. That is, only data nodes whose importance values reach a preset threshold value are entered into the database.
In summary, the method of the embodiments of the present disclosure may intelligently mine the data nodes of the target object and evaluate the importance of the data nodes. Because the method does not depend on information collection and experience judgment of experts, the error is smaller, and the efficiency is higher.
The following describes an exemplary importance quantifying method according to an embodiment of the present disclosure in conjunction with an actual application scenario.
The method provided by the embodiment of the specification is used for dynamically recording the data asset information of the enterprise, so that data support is provided for daily operation and maintenance, risk identification, risk processing and emergency of the enterprise.
Suppose that an enterprise adopts Unix as an information-based bottom operating system, and data nodes such as a router, a gateway, a switch and a server of the enterprise can run instructions of the Unix operating system.
In the process of recording the data asset information, firstly, a netstat link log of each data node in an enterprise is obtained based on a netstat instruction of a Unix operating system. The netstat link log records history information of links among the data nodes, and the history information includes data nodes which actively initiate the links and data nodes which passively initiate the links.
After the netstat link log is obtained, a link relation vector diagram of the data nodes is constructed based on information in the netstat link log.
The vector graph of the link relationship can be regarded as a topological structure of the link relationship between the data nodes. In this topology, the more links a data node corresponds to, the more important the data node is for the enterprise. Therefore, the total number of the data nodes as the start point of the link relation vector and the total number as the end point of the link relation vector can be counted later by the link relation vector diagram.
And then, for each data node, importing the total number serving as a starting point and the total number serving as an end point which are determined in a statistical manner into a pre-established Page Rank algorithm model, and calculating a Page Rank value corresponding to each data node by using the Page Rank algorithm model, wherein the Page Rank value is the weight of the data node.
Assuming that other quantization factors are not considered in the application scenario, the data node Page Rank value is directly used as the importance value of the data node Page Rank value.
And then, taking each data node as a data asset of the enterprise for inputting, and marking a corresponding importance value.
The above is a description of the method of the embodiments of the present specification. It will be appreciated that appropriate modifications may be made without departing from the principles outlined herein, and such modifications are intended to be included within the scope of the embodiments herein.
In correspondence with the above method, as shown in fig. 4, an embodiment of the present specification further provides an apparatus 400 for quantifying importance of a data asset, including:
the collecting module 410 obtains link log information of at least two data nodes of the target object.
As mentioned above, the target object may be an enterprise, a school, a hospital, etc., and the data node, i.e., the data infrastructure in the target object, may be a router, a gateway, a switch, a server, etc.
And the composition module 420 is used for constructing a vector diagram of the link relation of the at least two data nodes based on the link log information.
The link log information describes historical link information among the data nodes, and the data network structure of the target object can be determined through the link log information of each data node, so that the historical link quantity of each data node is obtained.
And the calculating module 430 determines the weight corresponding to the data node of the target object according to the link relation shown in the link relation vector diagram.
Specifically, the calculation module 430 may calculate the weight of each data node according to the total number of each data node shown in the link relation vector diagram as the starting point of the link relation vector and the total number of each data node as the ending point of the link relation vector based on the link analysis algorithm.
The quantization module 440 performs quantization evaluation on the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
It should be noted that the quantization method is not exclusive, and the embodiments of the present specification are not limited to this specifically. In this step, if the weight is used as the only factor for quantizing the importance of the data node, the weight is the importance.
Based on the importance quantification device shown in fig. 4, it can be known that, in the solution in the embodiment of the present specification, a link relation vector diagram between data nodes is generated according to link log information of the data nodes of a target object, so as to quantitatively evaluate the importance of the data nodes according to the link relation shown in the link relation vector diagram, thereby providing technical support for data risk early warning and data emergency processing. The whole scheme can be mechanically executed based on a computer program, so that the method has higher processing efficiency and accuracy, and is particularly suitable for dynamically mining the data assets of medium-sized and large-sized enterprises.
Optionally, the composition module 420 constructs a link relationship network of the at least two data nodes based on the link log information. And then, converting the link relation network into a link relation vector graph, wherein in each link relation vector of the link relation vector graph, the data node which actively initiates the link is used as a starting point of the link relation vector, and the data node which initiates the link is used as an end point of the link relation vector.
Optionally, when the calculating module 430 is executed, specifically based on a link analysis algorithm, the weight of each data node is calculated according to a total number of each data node shown in the link relation vector diagram as a start point of the link relation vector and a total number of each data node shown in the link relation vector diagram as an end point of the link relation vector.
Optionally, the at least two data nodes run a uinius Unix operating system, and the collecting module 410 is executed to collect link log information of the at least two data nodes of the target object, specifically based on a netstat instruction of the Unix operating system.
Optionally, the apparatus of this specification embodiment further includes:
and the logging module is used for performing associated logging on the IP addresses of the at least two data nodes and the corresponding importance values.
Optionally, the at least two data nodes comprise at least one of a route, a gateway, a switch, and a server of the target object.
Optionally, the link relation algorithm comprises any one of a hyperlink inducement topic search algorithm and a network ranking algorithm.
Obviously, the importance quantizing device according to the embodiment of the present specification can be used as the execution subject of the importance quantizing method shown in fig. 1, and thus can realize the functions of the importance quantizing method realized in fig. 1 to 3. Since the principle is the same, the detailed description is omitted here.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present specification. Referring to fig. 5, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be linked to each other by an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the importance quantifying device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
Based on the importance quantification device shown in fig. 5, it can be known that, in the solution in the embodiment of the present specification, a link relation vector diagram between data nodes is generated according to link log information of the data nodes of a target object, so as to quantitatively evaluate the importance of the data nodes according to the link relation shown in the link relation vector diagram, thereby providing technical support for data risk early warning and data emergency processing. The whole scheme can be mechanically executed based on a computer program, so that the method has higher processing efficiency and accuracy, and is particularly suitable for dynamically mining the data assets of medium-sized and large-sized enterprises.
The importance quantization method disclosed in the embodiment of fig. 1 in this specification can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
It should be understood that the electronic device in the embodiments of this specification may implement the functions of the importance quantifying apparatus in the embodiments shown in fig. 1 to fig. 3, and will not be described herein again.
Of course, besides the software implementation, the electronic device in this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Furthermore, the present specification embodiments also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 1, and in particular to perform the following method:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
It should be understood that the above-mentioned instructions, when executed by a portable electronic device including a plurality of application programs, can enable the importance quantifying apparatus described above to implement the functions of the embodiments shown in fig. 1 to 3, and will not be described in detail herein.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification. Moreover, all other embodiments obtained by a person skilled in the art without making any inventive step shall fall within the scope of protection of this document.
Claims (10)
1. A method of quantifying importance of data assets, comprising:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,
constructing a link relation vector graph of the at least two data nodes based on the link log information, including:
constructing a link relation network of the at least two data nodes based on the link log information;
and converting the link relation network into a link relation vector diagram, wherein in each link relation vector of the link relation vector diagram, the data node which actively initiates the link is used as a starting point of the link relation vector, and the data node which initiates the link is used as an end point of the link relation vector.
3. The method of claim 2, wherein the first and second light sources are selected from the group consisting of,
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram, wherein the determining comprises:
and calculating the weight of each data node according to the total number of each data node shown in the link relation vector diagram as the starting point of the link relation vector and the total number of each data node as the ending point of the link relation vector based on a link analysis algorithm.
4. The method of any one of claims 1-3,
the at least two data nodes run a UNIX operating system, and collect link log information of the at least two data nodes of the target object, including:
collecting link log information of at least two data nodes of the target object based on a netstat instruction of the Unix operating system.
5. The method of any of claims 1-3, further comprising:
and performing associated entry on the IP addresses of the at least two data nodes and the corresponding importance values.
6. The method of any one of claims 1-3,
the at least two data nodes include at least one of a route, a gateway, a switch, and a server of the target object.
7. The method according to claim 3,
the link relation algorithm includes any one of a hyperlink induced topic search algorithm and a network ranking algorithm.
8. An apparatus for quantifying importance of data assets, comprising:
the collection module is used for acquiring the link log information of at least two data nodes of the target object;
the composition module is used for constructing a link relation vector graph of the at least two data nodes based on the link log information;
the calculation module is used for determining the weight corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and the quantization module is used for performing quantization evaluation on the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
9. An electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
10. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring link log information of at least two data nodes of a target object;
constructing a link relation vector graph of the at least two data nodes based on the link log information;
determining a weight value corresponding to the data node of the target object according to the link relation shown by the link relation vector diagram;
and quantitatively evaluating the importance degree of the data node of the target object based on the weight value corresponding to the data node of the target object.
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