CN112988522A - Method, device and equipment for alarm signal association - Google Patents
Method, device and equipment for alarm signal association Download PDFInfo
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- CN112988522A CN112988522A CN202110251666.4A CN202110251666A CN112988522A CN 112988522 A CN112988522 A CN 112988522A CN 202110251666 A CN202110251666 A CN 202110251666A CN 112988522 A CN112988522 A CN 112988522A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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Abstract
The application relates to the technical field of alarm signal association and discloses a method for alarm signal association, which comprises the following steps: acquiring a plurality of alarm signals; determining a characterization node corresponding to the alarm signal; determining a communication relation among a plurality of characterization nodes; according to the obtained alarm signal, the characterization node corresponding to the alarm signal and the communication relation of the characterization node are determined, the alarm signal is associated according to the communication relation of the characterization node, the alarm signal can be automatically associated, and the efficiency of alarm signal association is improved. The application also discloses a device and equipment for associating the alarm signal.
Description
Technical Field
The present application relates to the field of alarm signal association technologies, and for example, to a method, an apparatus, and a device for alarm signal association.
Background
At present, in an operation and maintenance monitoring mode of equipment and a system, a large number of alarm signals are generated, operation and maintenance personnel monitor the operation condition of the equipment or the system from the large number of alarm signals, and decision processing aiming at the alarm signals still mainly takes manual work at present.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art: the alarm signals need to be correlated manually to mine possible alarm events, and the alarm signal processing efficiency is low.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method, a device and equipment for alarm signal association, so as to improve the efficiency of associating alarm signals.
In some embodiments, a method for alert signal association includes:
acquiring a plurality of alarm signals;
determining a characterization node corresponding to the alarm signal;
determining a communication relation among a plurality of characterization nodes;
and performing alarm signal association according to the communication relation of the representation nodes.
In some embodiments, the means for alert signal association comprises: a processor and a memory storing program instructions, the processor being configured to, upon execution of the program instructions, perform the method for alert signal association as described above.
In some embodiments, the apparatus comprises the above-described means for alert signal association.
The method, the device and the equipment for associating the alarm signal provided by the embodiment of the disclosure can realize the following technical effects: according to the obtained alarm signal, the characterization node corresponding to the alarm signal and the communication relation of the characterization node are determined, the alarm signal is associated according to the communication relation of the characterization node, the alarm signal can be automatically associated, and the efficiency of alarm signal association is improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic diagram of a method for alert signal association provided by an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating a model structure of a self-encoder provided by an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an apparatus for alarm signal association provided by an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
With reference to fig. 1, an embodiment of the present disclosure provides a method for alarm signal association, including:
step S101, acquiring a plurality of alarm signals;
step S102, determining a characterization node corresponding to the alarm signal;
step S103, determining the communication relation among a plurality of characterization nodes;
and step S104, performing alarm signal association according to the communication relation of the characterization nodes.
By adopting the method for associating the alarm signals, the characterization nodes corresponding to the alarm signals and the communication relation of the characterization nodes are determined according to the obtained alarm signals, the alarm signals are associated according to the communication relation of the characterization nodes, the alarm signals can be automatically associated, and the efficiency of associating the alarm signals is improved.
Optionally, the alert signal comprises: non-device alarm signals and device alarm signals that have occurred historically.
Optionally, determining a characterization node corresponding to the alarm signal includes: generating a graph structure according to the alarm signals, wherein the nodes of the graph structure are all the alarm signals; acquiring edges among all the alarm signals; and determining a characterization node corresponding to the alarm signal according to the node and the edge.
Optionally, generating a graph structure according to the alarm signal includes: selecting alarm signals in a preset time period, and sequentially connecting the selected alarm signals according to the time stamp sequence; and taking a connecting line between the two alarm signals as an edge, and taking the selected alarm signal as a node to form a graph structure. Optionally, the preset time period is 60 s.
Optionally, determining a characterization node corresponding to the alarm signal includes: and generating a graph structure according to the alarm signal and the equipment corresponding to the alarm signal, and determining a representation node corresponding to the alarm signal according to the node and the edge of the graph structure.
Optionally, generating a graph structure according to the alarm signal and the device corresponding to the alarm signal includes: selecting alarm signals in a preset time period, sequentially connecting the selected alarm signals according to the time stamp sequence, and connecting each alarm signal with corresponding equipment; and (4) taking the connecting lines between the alarm signals and the equipment as edges, and taking the selected alarm signals and the equipment corresponding to each alarm signal as nodes to form a graph structure.
Optionally, determining a characterization node corresponding to the alarm signal according to the node and the edge includes: generating a characteristic matrix corresponding to the node; generating an adjacency matrix corresponding to the nodes and the edges; and inputting the characteristic matrix and the adjacent matrix into a preset GAE model of a graph self-encoder to obtain a characterization node corresponding to the alarm signal.
Optionally, generating a feature matrix corresponding to the node includes: acquiring text characteristics of nodes; and converting the text features into a digital feature matrix through One-Hot (One-Hot) coding, and taking the digital feature matrix as a feature matrix.
Optionally, the textual features of the nodes include one or more of typical intervals, typical devices, signal features, states.
Optionally, the adjacency matrix is a matrix of connection relationships between nodes.
Therefore, the text features of the nodes are extracted to construct the feature matrix, so that the features of the nodes can be better highlighted, the association of the alarm signals is more accurate, the association of the alarm signals is facilitated, the association efficiency of the alarm signals is improved, and the potential alarm events can be conveniently found.
Optionally, the inputting the feature matrix and the adjacency matrix into a preset graph self-encoder GAE model to obtain a characterization node corresponding to the alarm signal includes: inputting the characteristic matrix and the adjacent matrix into a preset GAE model of a graph self-encoder to be trained; and extracting a characterization node corresponding to the alarm signal from the hidden variable layer of the trained GAE model.
In some embodiments, in order to obtain a low-dimensional vector representation of high-dimensional graph data, nodes in a graph structure are represented as low-dimensional vectors by preserving the network topology and node content information of the graph, so that the connection relationship between the nodes can be processed through a simple machine learning algorithm. Alternatively, the method is implemented by a graph autoencoder GAE, a graph neural network model for unsupervised extraction of graphs embeddingUnsupervised extraction of the profile data embedding is now performed. As shown in fig. 2, the self-encoder optionally includes an encoder, an intermediate hidden variable layer, and a decoder. Alternatively, the encoder of the graph self-encoder is constituted by a graph convolution layer GCN. Inputting the characteristic matrix X and the adjacent matrix A into an Encoder Encoder of GAE, outputting an implicit variable Z by the GAE Encoder, and obtaining the implicit variable Z by calculating Z as GCN (X, A); adjacent matrix reconstructed by decoding Decoder output with hidden variable Z
By calculation of
Obtaining a reconstructed adjacency matrix
Wherein Z is an implicit variable, X is a feature matrix, A is an adjacency matrix,
for reconstructed adjacency matrices, ZTIs the transpose of the hidden variable,
is an activation function.
Optionally, the characterizing node is embedding of the node. Optionally, the vector dimension of the characterization node is determined according to the number m of the nodes of the corresponding extracted hidden variable layer. Alternatively, each node of the graph structure can generate a 1 × m vector representation, i.e., a token node.
In this way, the representation nodes are extracted from the hidden variable layer of the graph self-encoder, and the information of the nodes can be effectively represented. And the alarm signal corresponding to the characterization node is associated according to the connection relation of the characterization node, so that the accuracy of alarm signal association can be improved.
Optionally, determining a connectivity relationship between a plurality of characterization nodes includes: acquiring event edge labels among all the characterization nodes; and determining the communication relation between the corresponding characterization nodes according to the event edge labels.
Optionally, obtaining an event edge label between each characterization node includes: splicing the characterization nodes pairwise to obtain spliced event edges; and inputting the event edges into a preset classifier to obtain event edge labels among the characterization nodes.
Optionally, pairwise splicing the characterization nodes to obtain spliced event edges, including: connecting the characterization nodes pairwise, and taking a connecting line between the two characterization nodes as an event edge.
Optionally, the classifier is obtained by acquiring an event edge sample with an event edge label and inputting the event edge sample into a random forest classification algorithm model for training to obtain the classifier.
Optionally, obtaining an event edge sample with an event edge label includes: two nodes s1 and s2 belonging to the same event, and the constructed event edge sample is the concatenation of the characterization nodes of s1 and s2 [ s1 ]embedding,s2embedding]The event edge label of the event edge sample is 1. Two nodes s3 and s4 which do not belong to the same event, and the constructed event edge sample is the concatenation of the characterization nodes of s3 and s4 [ s3 ]embedding,s4embedding]The event edge label of the event edge sample is 0.
In one embodiment, the random forest is an ensemble learning model formed by a plurality of regression classification trees, wherein the mathematical model of a single base decision tree is Y ═ f (X), where X is an event-side training data set with event-side labels and includes H event-side features, and D is an original training set and includes H feature numbers. Carrying out playback random sampling in an original training set by adopting a Bagging (leading aggregation algorithm) method to obtain k training subsets D1, D2, … … and Dk with the same number as that of the samples in the training set D; respectively constructing a base decision tree for the data of each training subset; in the construction of each decision tree, firstly, randomly selecting H characteristics from original H characteristics to construct an attribute set, wherein H is less than H, and H and H are positive integers; and combining the k base decision trees constructed based on the k training subsets in a voting or averaging mode to form a final random forest model, namely a classifier. Optionally, the preset condition is that when the number of training samples in the node is less than a set threshold, the number of iterations of the decision tree reaches a set value.
Optionally, determining a connectivity relationship between corresponding characterization nodes according to each event edge label includes: determining that a communication relation exists between the characterization nodes corresponding to the event edge labels under the condition that the event edge labels are the first preset threshold; and/or determining that no communication relation exists between the characterization nodes corresponding to the event edge labels under the condition that the event edge labels are the second preset threshold. Optionally, the first preset threshold is 1. Optionally, the second preset threshold is 0.
Therefore, the event edge labels of the characterization nodes are automatically obtained through the classifier, the communication relation of the characterization nodes is determined through the event edge labels, the communication relation among the characterization nodes can be automatically determined, the alarm signals are automatically correlated, and the efficiency of alarm signal correlation is improved. Meanwhile, according to the associated alarm signal, the potential alarm event is convenient to discover.
Optionally, performing alarm signal association according to the connectivity of the characterization node, including: and associating the alarm signals corresponding to the group of characterization nodes with the connectivity relation to obtain an alarm signal connectivity subgraph.
Therefore, the characterization node corresponding to the alarm signal and the communication relation of the characterization nodes are determined according to the obtained alarm signal, and the alarm signal is associated according to the communication relation of the characterization nodes, so that the alarm signal can be automatically associated, the association efficiency of the alarm signal is improved, and the potential alarm event can be conveniently found.
As shown in fig. 3, an apparatus for alarm association according to an embodiment of the present disclosure includes a processor (processor)100 and a memory (memory)101 storing program instructions. Optionally, the apparatus may also include a Communication Interface (Communication Interface)102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via a bus 103. The communication interface 102 may be used for information transfer. The processor 100 may call program instructions in the memory 101 to perform the method for alert signal association of the above-described embodiments.
Further, the program instructions in the memory 101 may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 101, which is a computer-readable storage medium, may be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing, i.e. implements the method for alert signal association in the above embodiments, by executing program instructions/modules stored in the memory 101.
The memory 101 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 101 may include a high-speed random access memory, and may also include a nonvolatile memory.
By adopting the device for associating the alarm signals, which is provided by the embodiment of the disclosure, the characterization nodes corresponding to the alarm signals and the communication relations of the characterization nodes are determined according to the obtained alarm signals, and the alarm signals are associated according to the communication relations of the characterization nodes, so that the alarm signals can be automatically associated, the association efficiency of the alarm signals is improved, and the potential alarm events can be conveniently found.
The embodiment of the present disclosure provides an apparatus including the above apparatus for alarm signal association. According to the obtained alarm signal, the characterization node corresponding to the alarm signal and the communication relation of the characterization node are determined, the alarm signal is associated according to the communication relation of the characterization node, the alarm signal can be automatically associated, the alarm signal association efficiency is improved, and the potential alarm event can be conveniently found.
Optionally, the device comprises a mobile phone and a computer.
Embodiments of the present disclosure provide a computer-readable storage medium having stored thereon computer-executable instructions configured to perform the above-described method for alert signal association.
Embodiments of the present disclosure provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for alert signal correlation.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for alert signal association, comprising:
acquiring a plurality of alarm signals;
determining a characterization node corresponding to the alarm signal;
determining a connectivity relationship between the plurality of characterization nodes;
and performing alarm signal association according to the communication relation of the characterization nodes.
2. The method of claim 1, wherein determining the characterization node to which the alarm signal corresponds comprises:
generating a graph structure according to the alarm signals, wherein the nodes of the graph structure are all the alarm signals;
acquiring edges among the alarm signals;
and determining a characterization node corresponding to the alarm signal according to the node and the edge.
3. The method of claim 2, wherein determining the characterization node corresponding to the alarm signal according to the node comprises:
generating a feature matrix corresponding to the node; generating an adjacency matrix corresponding to the nodes and the edges;
and inputting the characteristic matrix and the adjacent matrix into a preset GAE model of a graph self-encoder to obtain a characterization node corresponding to the alarm signal.
4. The method according to claim 3, wherein inputting the feature matrix and the adjacency matrix into a preset GAE (graph auto-encoder) model to obtain a characterization node corresponding to the alarm signal comprises:
inputting the characteristic matrix and the adjacency matrix into a preset GAE model of a graph self-encoder for training;
and extracting a characterization node corresponding to the alarm signal from the hidden variable layer of the trained GAE model.
5. The method of claim 4, wherein determining connectivity relationships between the plurality of characterization nodes comprises:
acquiring event edge labels among the characterization nodes;
and determining the communication relation between corresponding characterization nodes according to each event edge label.
6. The method of claim 5, wherein obtaining event edge labels between the characterization nodes comprises:
splicing the characterization nodes pairwise to obtain spliced event edges;
and inputting the event edges into a preset classifier to obtain event edge labels among the characterization nodes.
7. The method of claim 5, wherein determining connectivity between corresponding characterization nodes according to each event edge label comprises:
under the condition that the event edge label is a first preset threshold value, determining that a communication relation exists between the characterization nodes corresponding to the event edge label; and/or the presence of a gas in the gas,
and under the condition that the event edge label is a second preset threshold value, determining that no communication relation exists between the characterization nodes corresponding to the event edge label.
8. The method according to any one of claims 1 to 7, wherein performing alarm signal association according to the connectivity relationship of the characterization nodes comprises:
and associating the alarm signals corresponding to the group of characterization nodes with the connectivity relation to obtain an alarm signal connectivity subgraph.
9. An apparatus for alert signal association comprising a processor and a memory having stored thereon program instructions, wherein the processor is configured to perform the method for alert signal association of any of claims 1 to 8 when executing the program instructions.
10. A device comprising an apparatus for alarm signal association according to claim 9.
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