CN111612300A - Scene anomaly perception index calculation method and system based on deep hybrid cloud model - Google Patents
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Abstract
The invention relates to the technical field of index calculation, in particular to a scene abnormity perception index calculation method and system based on a deep hybrid cloud model. The method comprises the following steps: graph coding is carried out on the network resource nodes by using graph encoding; establishing a cloud model evaluation scale by using an analytic hierarchy process based on deep learning; and carrying out online scene abnormity perception detection by using a cloud scale. In the scene abnormity perception index calculation method and system based on the deep hybrid cloud model, graph encoding is carried out on network resource nodes by using graph embedding, the method has wider application range and generalization capability, the encoded node resources are constructed into the whole cloud model, a cloud model evaluation scale is established by using an analytic hierarchy process based on deep learning, scene abnormity perception detection is carried out, detection can be carried out under data of different periodic dimensions, a training model is used for online detection, and large-scale and dynamically-changing network resources can be detected in real time.
Description
Technical Field
The invention relates to the technical field of index calculation, in particular to a scene abnormity perception index calculation method and system based on a deep hybrid cloud model.
Background
The existing detection system cannot adapt to large-scale and dynamically-changed network resources, only can sense abnormal points in the whole network, and cannot accurately position the positions of the abnormal points.
Disclosure of Invention
The invention aims to provide a scene abnormity perception index calculation method and a scene abnormity perception index calculation system based on a deep hybrid cloud model, so as to solve the problems in the background technology.
In order to solve the above technical problems, an object of the present invention is to provide a scene anomaly awareness index calculation method based on a deep hybrid cloud model, which includes the following steps:
s1, carrying out graph coding on the network resource nodes by using graph encoding;
s2, establishing a cloud model evaluation scale by using an analytic hierarchy process based on deep learning;
and S3, carrying out online scene abnormity perception detection by using a cloud scale.
Preferably, in S1, the method for graph-coding the network resource node by using graphimbedding specifically includes the following steps:
s1.1, inputting: graph (nodes in the Graph represent network resources such as servers and the like, the nodes (network resources) in each Graph contain respective attributes such as storage capacity, occupancy rate, bandwidth and the like; the weight of edges (edges) between the nodes in the Graph can embody direct or indirect dependence relationship of the network resources with each other);
s1.2, outputting: graph encoding representation based on GraphEmbedding for network resources;
s1.3, initialization: a adjacency matrix based on network resources (nodes), a feature matrix based on network resource (node) attributes;
s1.4, performing iterative training of GraphEmbedding.
Preferably, in S1.4, the method for iteratively training grapphembedding specifically includes the following steps:
s1.4.1, for the Graph (Graph) formed by the network resources (nodes), firstly, analyzing the spatial structure of the Graph, and aggregating the lower order and the higher order spatial relations of any node (node) through an aggregation function to form a new representation of the spatial relations of the node (node);
s1.4.2, aggregating the characteristic relation of nodes (nodes) known in the initialization stage for the aggregated spatial relation formed in S1.4.1, and fusing the spatial relation (spatialrelationship) and the characteristic relation of network resources (nodes) by an IoT-GraphEmaddressing algorithm;
s1.4.3, optimizing the network structure through multiple training to form the optimal graph code representation.
Preferably, in S2, the method for establishing the cloud model evaluation scale by using the deep learning-based analytic hierarchy process specifically includes the following steps:
s2.1, scaling 0.1-0.9 to 9 cloud models (Exi, Eni, Hei) respectively (i ═ 1, 2, … …, 9), where 0.1, 0.2, …,0.9 correspond to the expected Ex1, Ex2, … …, Ex9 of the cloud models respectively;
s2.2, setting the discourse domain U of 9 cloud models as [0.1,0.9 ]]Expected value of each cloud modelIs Ex1＝0.1、Ex2＝0.2、……、Ex9Obtaining the entropy and the super-entropy of each cloud model according to a golden section method, wherein the entropy of each cloud model is as follows: en1＝En3＝En5＝En7＝En9＝0.0707，En2＝En4＝En6＝En80.0437; hyper-entropy of each cloud model: he (He)1＝He3＝He5He7 ═ He9 ═ 0.0118, He2 ═ He4 ═ He6 ═ He8 ═ 0.0073, and thus the scales expressed by the cloud model (Exi, En) can be obtained (Exi, En, respectively)i、Hei)(i＝1、2、……、9)；
S2.3, constructing a judgment matrix, and setting the judgment matrix in a domain of discourse [ Umin，Umax]Wherein m adjacent basis clouds C1 ═ e (Ex)1，En1，He1)、……、Cm＝(Exm，Enm，Hem) And aggregating m clouds to obtain a qualitative floating cloud C ═ (Ex, En, He), wherein the numerical index calculation formula is as follows:
another object of the present invention is to provide a scene anomaly awareness index calculation system based on a deep hybrid cloud model, including:
the graph coding module is used for carrying out graph coding on the network resource nodes by using graph coding;
the cloud model establishing module is used for establishing a cloud model evaluation scale by using a deep learning-based analytic hierarchy process;
and the perception detection module is used for carrying out online scene abnormity perception detection by using the cloud scale.
The third objective of the present invention is to provide a scene abnormity perception index calculation device based on a deep hybrid cloud model, which includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the scene abnormity perception index calculation method based on the deep hybrid cloud model when executing the computer program.
The fourth objective of the present invention is to provide a computer-readable storage medium, wherein at least one program is stored in the storage medium, and the at least one program is executed by the processor to implement the steps of the scene anomaly awareness index calculation method based on the deep hybrid cloud model as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that: in the scene abnormity perception index calculation method and system based on the deep hybrid cloud model, graph encoding is used for representing network resource nodes, compared with the traditional numerical index calculation, more types of network resource nodes can be brought into analysis, such as a server, a database, a process, an ammeter, bandwidth and the like, nondifferential analysis is carried out, the method has wider application range and generalization capability, the encoded node resources are constructed into an integral cloud model, a cloud model evaluation scale is established by using an analytic hierarchy process based on deep learning, scene abnormity perception detection is carried out, detection can be carried out under data of different periodic dimensions, and compared with the traditional fixed period detection, more use requirements can be met. The training model is used for online detection, and can detect large-scale and dynamically-changed network resources in real time, so that most actual production environments are met.
Drawings
FIG. 1 is an overall process flow diagram of the present invention;
FIG. 2 is a flowchart of a method for graph coding network resource nodes by using graphimbedding according to the present invention;
FIG. 3 is a flowchart of an iterative GraphEmbelling training method of the present invention;
FIG. 4 is a flow chart of a method of the present invention for establishing a cloud model evaluation scale using deep learning based analytic hierarchy process;
FIG. 5 is a block diagram of a scene anomaly awareness index calculation system based on a deep hybrid cloud model according to the present invention;
fig. 6 is a configuration diagram of an index calculation device according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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. 1-6, the present invention provides a technical solution:
the invention provides a scene abnormity perception index calculation method based on a deep mixed cloud model, which comprises the following steps:
s1, carrying out graph coding on the network resource nodes by using graph encoding;
s2, establishing a cloud model evaluation scale by using an analytic hierarchy process based on deep learning;
and S3, carrying out online scene abnormity perception detection by using a cloud scale.
In this embodiment, in S1, the method for performing graph coding on a network resource node by using graphembedding specifically includes the following steps:
s1.1, inputting: graph (nodes in the Graph represent network resources such as servers and the like, the nodes (network resources) in each Graph contain respective attributes such as storage capacity, occupancy rate, bandwidth and the like; the weight of edges (edges) between the nodes in the Graph can embody direct or indirect dependence relationship of the network resources with each other);
s1.2, outputting: graph encoding representation based on GraphEmbedding for network resources;
s1.3, initialization: a adjacency matrix based on network resources (nodes), a feature matrix based on network resource (node) attributes;
s1.4, performing iterative training of GraphEmbedding.
Further, in S1.4, the method for iteratively training grapphembedding specifically includes the following steps:
s1.4.1, for the Graph (Graph) formed by the network resources (nodes), firstly, analyzing the spatial structure of the Graph, and aggregating the lower order and the higher order spatial relations of any node (node) through an aggregation function to form a new representation of the spatial relations of the node (node);
s1.4.2, aggregating the characteristic relation of nodes (nodes) known in the initialization stage for the aggregated spatial relation formed in S1.4.1, and fusing the spatial relation (spatialrelationship) and the characteristic relation of network resources (nodes) by an IoT-GraphEmaddressing algorithm;
s1.4.3, optimizing the network structure through multiple training to form the optimal graph code representation.
Specifically, in S2, the method for establishing the cloud model evaluation scale by using the deep learning-based analytic hierarchy process specifically includes the following steps:
s2.1, scaling 0.1-0.9 to 9 cloud models (Exi, Eni, Hei) respectively (i ═ 1, 2, … …, 9), where 0.1, 0.2, …,0.9 correspond to the expected Ex1, Ex2, … …, Ex9 of the cloud models respectively;
s2.2, setting the discourse domain U of 9 cloud models as [0.1,0.9 ]]The expected value of each cloud model is Ex1＝0.1、Ex2＝0.2、……、Ex9Obtaining the entropy and the super-entropy of each cloud model according to a golden section method, wherein the entropy of each cloud model is as follows: en1＝En3＝En5＝En7＝En9＝0.0707，En2＝En4＝En6＝En80.0437; hyper-entropy of each cloud model: he (He)1＝He3＝He5Scale represented by cloud model (Exi, En) can be obtained by He7 ═ He9 ═ 0.0118, He2 ═ He4 ═ He6 ═ He8 ═ 0.0073i、Hei)(i＝1、2、……、9)；
S2.3, constructing a judgment matrix, and setting the judgment matrix in a domain of discourse [ Umin，Umax]Wherein m adjacent basis clouds C1 ═ e (Ex)1，En1，He1)、……、Cm＝(Exm，Enm，Hem) And aggregating m clouds to obtain a qualitative floating cloud C ═ (Ex, En, He), wherein the numerical index calculation formula is as follows:
another object of the present invention is to provide a scene anomaly awareness index calculation system based on a deep hybrid cloud model, including:
the graph coding module is used for carrying out graph coding on the network resource nodes by using graph coding;
the cloud model establishing module is used for establishing a cloud model evaluation scale by using a deep learning-based analytic hierarchy process;
and the perception detection module is used for carrying out online scene abnormity perception detection by using the cloud scale.
It should be noted that the functions of the graph coding module, the cloud model building module, and the sensing detection module are described in detail with reference to the description of the method portion corresponding to each module, and are not described here again.
Referring to fig. 6, a schematic structural diagram of a scene anomaly awareness index calculation device based on a depth hybrid cloud model according to an embodiment of the present invention is shown, where the device includes a processor 101, a memory 102, and a bus 103.
The processor 101 comprises one or more processing cores, the processor 101 is connected with the processor 101 through a bus 103, the memory 102 is used for storing program instructions, and the scene anomaly awareness index calculation method based on the deep hybrid cloud model is realized when the processor 102 executes the program instructions in the memory 102.
Alternatively, memory 102 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
In addition, the present invention further provides a computer-readable storage medium, in which at least one program is stored, where the at least one program is executed by the processor to implement any of the steps of the method for calculating a scene abnormality perception index based on a depth hybrid cloud model.
Optionally, the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the steps of the scene anomaly awareness index calculation method based on the depth hybrid cloud model in the above aspects.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to instructions of a program, where the program may be stored in a computer readable storage medium, and the above mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A scene abnormity perception index calculation method based on a deep mixed cloud model comprises the following steps:
s1, carrying out graph coding on the network resource nodes by using graph encoding;
s2, establishing a cloud model evaluation scale by using an analytic hierarchy process based on deep learning;
and S3, carrying out online scene abnormity perception detection by using a cloud scale.
2. The scene anomaly awareness index calculation method based on the depth hybrid cloud model according to claim 1, wherein: in S1, the method for graph-coding network resource nodes using graphembedding specifically includes the following steps:
s1.1, inputting: graph represents network resources, and nodes in each Graph comprise respective attributes;
s1.2, outputting: graph encoding representation based on GraphEmbedding for network resources;
s1.3, initialization: based on the adjacency matrix of the network resource and the characteristic matrix of the network resource attribute;
s1.4, performing iterative training of GraphEmbedding.
3. The scene anomaly awareness index calculation method based on the depth hybrid cloud model according to claim 2, wherein: in S1.4, the method for iteratively training grapphembedding specifically includes the following steps:
s1.4.1, for the Graph formed by the network resources, firstly, analyzing the Graph space structure, and aggregating the low-order and high-order space relations of any node through an aggregation function to form a new space relation expression for the node;
s1.4.2, aggregating the characteristic relations to the nodes known in the initialization stage for the aggregated spatial relations formed in S1.4.1, and fusing the spatial relations and the characteristic relations of the network resources through an IoT-GraphEmbedding algorithm;
s1.4.3, optimizing the network structure through multiple training to form the optimal graph code representation.
4. The scene anomaly awareness index calculation method based on the depth hybrid cloud model according to claim 1, wherein: in S2, the method for establishing the cloud model evaluation scale using the deep learning-based analytic hierarchy process specifically includes the following steps:
s2.1, scaling 0.1-0.9 to 9 cloud models (Exi, Eni, Hei) respectively (i ═ 1, 2, … …, 9), where 0.1, 0.2, …,0.9 correspond to the expected Ex1, Ex2, … …, Ex9 of the cloud models respectively;
s2.2, setting the discourse domain U of 9 cloud models as [0.1,0.9 ]]The expected value of each cloud model is Ex1＝0.1、Ex2＝0.2、……、Ex90.9, each cloud model is obtained according to the golden section methodEntropy and hyper-entropy, wherein the entropy of each cloud model: en1＝En3＝En5＝En7＝En9＝0.0707，En2＝En4＝En6＝En80.0437; hyper-entropy of each cloud model: he (He)1＝He3＝He5He7 ═ He9 ═ 0.0118, He2 ═ He4 ═ He6 ═ He8 ═ 0.0073; the scales (Exi, En) expressed by the cloud model are obtainedi、Hei)(i＝1、2、……、9)；
S2.3, constructing a judgment matrix, and setting the judgment matrix in a domain of discourse [ Umin，Umax]Wherein m adjacent basis clouds C1 ═ e (Ex)1，En1，He1)、……、Cm＝(Exm，Enm，Hem) And aggregating m clouds to obtain a qualitative floating cloud C ═ (Ex, En, He), wherein the numerical index calculation formula is as follows:
5. a scene anomaly awareness index computing system based on a deep hybrid cloud model comprises:
the graph coding module is used for carrying out graph coding on the network resource nodes by using graph coding;
the cloud model establishing module is used for establishing a cloud model evaluation scale by using a deep learning-based analytic hierarchy process;
and the perception detection module is used for carrying out online scene abnormity perception detection by using the cloud scale.
6. The utility model provides a scene anomaly perception index computing device based on degree of depth hybrid cloud model which characterized in that: the method comprises a processor, a memory and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the scene anomaly awareness index calculation method based on the depth hybrid cloud model according to any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium, wherein at least one program is stored in the storage medium, and the at least one program is executed by the processor to implement the steps of the scene anomaly awareness index calculation method based on the depth hybrid cloud model according to any one of claims 1 to 4.
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