CN113961425B - Method, device and equipment for processing alarm message - Google Patents
Method, device and equipment for processing alarm message Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for processing an alarm message, wherein the method comprises the following steps: obtaining at least one warning message; merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm; calculating a semantic similarity between the at least one alert; and performing correlation processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event. By the mode, the method and the system can enable operation and maintenance personnel to extract valuable alarm event information from massive alarm information and effectively position the system abnormity, so that the alarm storm is effectively processed.
Description
Technical Field
The present invention relates to the field of operation and maintenance alarm processing technologies, and in particular, to a method, an apparatus, and a device for processing an alarm message.
Background
The alarm is an important means for monitoring the system by operation and maintenance personnel through a rule or algorithm so as to timely perform corresponding handling on the system abnormity, and meanwhile, the fault phenomenon of the system is further positioned by further analyzing the alarm, and the abnormity is solved by adopting a corresponding handling means.
In a real operation and maintenance scene, with the popularization of cloud computing and micro-service architectures, system service node topologies become increasingly complex, the completion of a specific service function requires the cooperation of service nodes of a system, and complex dependency relationships exist among the service nodes. When a system service node is abnormal to cause the node to be in fault, the fault is transmitted to all service links depending on the node, so that a large amount of alarm information exceeding the processing limit of operation and maintenance personnel is generated in a short time, and the phenomenon is called alarm storm.
Disclosure of Invention
The technical problem to be solved by the invention is how to provide a method, a device and equipment for processing an alarm message. The problem that the alarm storm can not be effectively processed in the prior art is solved.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for processing an alarm message comprises the following steps:
obtaining at least one warning message;
merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm;
calculating a semantic similarity between the at least one alert;
and performing correlation processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event.
Optionally, merging the at least one warning message by using a tree data structure to obtain at least one alarm, where the merging includes:
acquiring a merging parameter of an alarm message, wherein the merging parameter comprises an attribute of the alarm message and a first similarity threshold;
searching a tree data structure according to an attribute value list of the attribute of the alarm message in the merging parameters to obtain an alarm list stored by leaf nodes of the tree data structure; each layer of leaf node of the tree data structure stores an attribute value, and the attribute values of the alarm messages belonging to the same leaf node are the same;
calculating the similarity between the alarm message and the alarm list;
and combining the alarm messages according to the similarity between the alarm messages and the alarm list and the first similarity threshold value to obtain at least one alarm.
Optionally, merging the warning messages according to the similarity between the warning message and the warning list and the first similarity threshold, to obtain at least one warning, where the merging includes:
if the similarity between the alarm message and the alarm list is larger than the first similarity threshold, combining the alarm messages to obtain at least one alarm, and otherwise, creating a new alarm.
Optionally, the method for processing an alarm message further includes at least one of the following:
if the occurrence time of the alarm message is longer than the occurrence time of the alarm, updating the occurrence time of the alarm to the occurrence time of the alarm message; the occurrence time of the alarm is the occurrence time of the alarm message combined last in the alarm;
and taking the description information of the alarm message and the longest common subsequence LCS of the description information of the alarm as the updated description information of the alarm.
Optionally, calculating a semantic similarity between the at least one alarm includes:
obtaining a correlation parameter of an alarm;
and calculating the maximum semantic similarity and the average semantic similarity of the target alarm and the effective events in the event list according to the correlation parameters.
Optionally, the associated parameters are: (T)window,Vmax_sim,Vmean_sim,Wt,Whost,Wdesc）；
Wherein, TwindowIs the validity window time of the event, Vmax_simIs the maximum threshold value, V, of event correlationmean_simIs an average threshold of event correlation, WtFor calculating temporal similarity weights when similarity between alarms, WhostFor the calculation of the host similarity weight in the similarity between alarms, WdescThe similarity weight of the description information of the alarm message when the similarity between the alarms is calculated;
calculating the maximum semantic similarity v _ max of the target alarm and the effective events in the event listiSimilarity with average semantic v meaniAnd obtaining:
[(v_max1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]；
wherein n is the number of effective events in the event list;
taking the maximum value of the maximum semantic similarity list to obtain a corresponding first event incident _ a and a maximum value v _ max:
incident_a,v_max=(v_max1,v_max2,...,v_maxn)；
taking the maximum value of the average semantic similarity list to obtain a corresponding second event incident _ b and a maximum value v _ mean:
incident_b,v_mean=(v_mean1,v_mean2,...,v_meann)。
optionally, performing association processing on the at least one alarm according to semantic similarity between the at least one alarm to obtain at least one event, where the association processing includes:
if V _ max > Vmax_simAdding the alarm into the first event, and updating the first event, to a;
if V _ max > Vmax_simNot satisfied, but satisfies V _ mean > Vmean_simAdding the alarm to the second event incident _ b and updating the second event incident _ b;
if V _ max > Vmax_simNot satisfied, V _ mean > Vmean_simAnd if the condition is not met, an event is newly built and added into the event list.
An embodiment of the present invention further provides an apparatus for processing an alarm message, including:
the acquisition module is used for acquiring at least one alarm message;
the first merging module is used for merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm;
a calculation module for calculating semantic similarity between the at least one alert;
and the second merging module is used for performing association processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event.
An embodiment of the present invention further provides an electronic device, including: a processor, a memory storing a computer program which, when executed by the processor, performs the method of processing an alert message as described above.
Embodiments of the present invention also provide a computer-readable storage medium for storing instructions that, when executed on a computer, cause the computer to perform the method for processing an alert message as described above.
The scheme of the invention at least comprises the following beneficial effects:
by obtaining at least one alert message; merging the alarm messages by adopting a tree data structure to obtain at least one alarm; calculating a semantic similarity between the at least one alert; and performing correlation processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event. The method can enable operation and maintenance personnel to extract valuable alarm event information from massive alarm information and effectively locate the system abnormity, thereby effectively processing alarm storms.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for processing an alarm message according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for alarm merging based on a tree data structure according to an embodiment of the present invention;
FIG. 3 illustrates a flow chart of a method for semantic similarity based alert correlation provided by an embodiment of the present invention;
FIG. 4 is a flow chart of another method for processing an alarm message according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a time similarity calculation function provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram illustrating an apparatus for processing an alert message according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a method for processing an alarm message, including:
and 14, performing correlation processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event.
In the method for processing an alarm message according to this embodiment, at least one alarm message is obtained; merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm; calculating a semantic similarity between the at least one alert; and performing correlation processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event. The method can enable operation and maintenance personnel to extract valuable alarm information from massive alarm information and effectively locate the system abnormity, thereby effectively processing alarm storm.
In the embodiment of the present invention, the alert message is denoted as alert _ message, and the alert message includes at least one of the following attributes, specifically as follows:
the alert _ message _ id is the id of the alarm message;
the alert _ message _ time is the occurrence time of the alert message;
alert _ message _ description is the detailed description information of the alarm message;
alert _ message _ host is host information of the alarm message;
the alert _ message _ source is the source information in the alarm message;
alert _ message _ check is summary information of the alarm message, etc.
The alarm message has, but is not limited to, the above-mentioned alarm message attributes, and also has other alarm message attributes, and the specific alarm message attributes are determined by the characteristics of the service node.
The alert is denoted alert and is composed of a large number of repeated alert messages over a period of time, and comprises at least one of the following attributes:
alert _ id is the id of the alarm;
alert _ time is the last consolidated alert message time in the alert;
alert _ description is description information of the alert;
alert _ host is information of the source of the alert, etc.
The alert attribute has, but is not limited to, the alert attribute described above. An event, denoted as an incident, is composed of alarms with relevance within a time window, the event comprising at least one of the following attributes:
incedent _ last _ time is the time of the last associated alarm of the event;
the incident _ id is the id of the event, etc.
The event attributes have, but are not limited to, the event attributes described above.
As shown in fig. 2, in the method for alarm merging based on a tree data structure according to the embodiment of the present invention, step 12 may include:
step 121, obtaining a merging parameter of the warning message, where the merging parameter includes an attribute value and a first similarity threshold;
specifically, an alert message (alert _ message) merging parameter is defined, and a formula of the merging parameter is as follows: merge _ para = (fields, sim _ threshold), field = [ field _1, field _ 2., field _ n ], where field is a list of attributes and sim _ threshold is a first similarity threshold, taking the number of floating points between 0 and 1.
Step 122, searching a tree data structure according to the attribute value list of the attribute of the alarm message in the merging parameters to obtain an alarm list stored by leaf nodes of the tree data structure; each layer of leaf node of the tree data structure stores an attribute value, and the attribute values of the alarm messages belonging to the same leaf node are the same;
specifically, after receiving the alert message (alert _ message), the attribute value list of the alert message is obtained according to the attribute list fields in step 121: values = [ value _1, value _ 2., value _ n ], searching the tree data structure according to the attribute value list until the attribute values of the alarm messages of the leaf nodes belonging to the same leaf node of the tree data structure are all consistent, and finally acquiring the alarm stored on the leaf node. In this step, the first-layer leaf node of the tree data structure stores value _1, the second-layer leaf node of the tree data structure stores value _2, and so on. The leaf node stores a series of alarms, i.e., an alarm list.
Step 123, calculating the similarity between the alarm message and the alarm list;
specifically, according to the leaf node searched in step 122, a text similarity value sim _ value between alert _ message _ description of the alert message and alert _ description of the ith alarm is calculatediAnd sequentially calculating the similarity between each alarm message and the alarm list. The similarity calculation method described in this embodiment includes, but is not limited to, the following algorithms: hamming distance, jaro-winkler distance, jaccard distance.
And step 124, combining the warning messages according to the similarity between the warning messages and the warning list and the first similarity threshold value to obtain at least one warning.
In this embodiment, the alert messages are combined based on the tree data structure, and an alert is generated. The alert message merging refers to merging repeated alert messages (alert _ message) for a short time into one alert (alert).
In yet another alternative embodiment of the present invention, step 124 may comprise: if the similarity between the alarm message and the alarm list is larger than the first similarity threshold, combining the alarm messages to obtain at least one alarm, and otherwise, creating a new alarm.
In this embodiment, if sim _ valueiIf it is greater than sim _ threshold, then all alert messages are merged into the alert, while the time attribute (alert _ time) and description attribute (alert _ description) of the alert are updated. If sim _ valuei< sim _ threshold or sim _ valueiIf = sim _ threshold, an alert is newly created, the time attribute (alert _ time) of the newly created alert is the occurrence time (alert _ message _ time) of the alert message, and the description attribute (alert _ description) of the newly created alert is the specific description information (alert _ message _ description) of the alert message. The alarm contains only the newly incoming alert message and then returns the id information (alert _ id) of the alarm.
In yet another optional embodiment of the present invention, step 124 may further include at least one of the following:
step 125, if the occurrence time of the alarm message is longer than the occurrence time of the alarm, updating the occurrence time of the alarm to the occurrence time of the alarm message; the occurrence time of the alarm is the occurrence time of the alarm message combined last in the alarm.
Specifically, if the time attribute (alert _ message _ time) of the alert message is greater than the occurrence time attribute (alert _ time) of the alert, the alert _ time is updated to alert _ message _ time.
And step 126, taking the description information of the alarm message and the longest common subsequence LCS of the description information of the alarm as the updated description information of the alarm.
Specifically, the LCS between the description attribute (alert _ message _ description) of the alert message and the description attribute (alert _ description) of the alert is calculated as the updated description attribute (alert _ description) of the alert. The LCS described in this embodiment is the longest common subsequence.
As shown in fig. 3, in the method for associating an alarm based on semantic similarity according to an embodiment of the present invention, step 13 may include:
step 131, obtaining the related parameters of the alarm;
the associated parameters are: (T)window,Vmax_sim,Vmean_sim,Wt,Whost,Wdesc）
Wherein, TwindowIs the validity window time of the event, Vmax_simIs the maximum threshold value, V, of event correlationmean_simIs an average threshold of event correlation, WtFor calculating temporal similarity weights when similarity between alarms, WhostFor the calculation of the host similarity weight in the similarity between alarms, WdescThe similarity weight of the description information of the alarm message when the similarity between the alarms is calculated;
step 132, according to the correlation parameters, calculating the maximum semantic similarity and the average semantic similarity between the target alarm and the effective events in the event list.
In yet another alternative embodiment of the present invention, step 132 may comprise:
calculating the maximum semantic similarity v _ max of the target alarm and the effective events in the event listiSimilarity with average semantic v meaniObtaining:
[(v_max1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]
wherein n is the number of effective events in the event list;
and (3) maximizing the maximum semantic similarity list to obtain a corresponding event incident _ a and a maximum value v _ max:
incident_a,v_max=(v_max1,v_max2,...,v_maxn)
and (3) maximizing the average semantic similarity list to obtain a corresponding event incident _ b and a maximum value v _ mean:
incident_b,v_mean=(v_mean1,v_mean2,...,v_meann)。
specifically, an alarm alert _ input is received online, and the maximum semantic similarity v _ max between the alarm alert _ input and the effective events in the event list is calculated in turniSimilarity with average semantic v meaniObtaining [ (v _ max)1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]. Where n is the number of active events in the event list. And (3) maximizing the maximum semantic similarity list to obtain a corresponding event incident _ a and a maximum value v _ max: incident _ a, v _ max = (v _ max)1,v_max2,...,v_maxn). And (3) taking the maximum value of the average semantic similarity list to obtain a corresponding event incident _ b and a maximum value v _ mean: incident _ b, v _ mean = (v _ mean)1,v_mean2,...,v_meann)。
In yet another alternative embodiment of the present invention, step 14 may comprise:
if V _ max > Vmax_simAdding the alarm into the first event, and updating the first event, to a;
if V _ max > Vmax_simDoes not satisfy, but satisfies the following conditions: v _ mean > Vmean_simAdding the alarm to the second event incident _ b and updating the second event incident _ b;
if V _ max > Vmax_simNot satisfied, V _ mean > Vmean_simAnd if the condition is not met, an event is newly built and added into the event list.
Specifically, if V _ max > Vmax_simAssociating the alarm alert _ input with the first event (inciden _ a), i.e. adding the alarm to the first time (inciden _ a) and updating the first event; if the above condition is not satisfied but the following condition V _ mean > V is satisfiedmean_simThe alarm alert _ input is associated with the second event (incident _ b), i.e. the alarm is added to (incident _ b) and the event is updated. If the condition is not satisfiedAn event incident is created and added to the event list.
In this embodiment, alarms that are semantically of the same type are associated. And (4) associating adjacent alarms by utilizing an online clustering method by calculating semantic similarity between the alarms. The alarm association refers to associating alarms with intrinsic relevance as an event. Typical methods for associating alarms are rule-based alarm association methods, such as associating alarms from the same service node or associating alarms belonging to the same business.
As shown in fig. 4, another method for denoising an alert message according to an embodiment of the present invention, after obtaining at least one event based on the steps 11 to 14, may further include:
specifically, for the event judgment, calculating the difference value between the alert _ time of the alert _ input and the incident _ last _ time of the event in the event list, if the alert _ time-incident _ last _ time > TwindowIf the event is overdue, the subsequent similarity calculation is not carried out;
As shown in fig. 5, in the time similarity calculation function graph provided in the embodiment of the present invention, specifically, calculating the maximum similarity v _ max and the average similarity v _ mean between the alarm alert _ input and the event includes:
sequential and event incidentiThe alarm _ j in (1) calculates the similarity v _ simi：v_simj=Sim(alert_input,alert_j)；
The method for calculating the similarity of the alarm and the alarm comprises the following steps: sim (alert _1, alert _2) = Wt*simt(t1,t2)+Whost*simhost(host1,host2)+Wdesc*simdesc(desc1,desc2). Sim in the above formulat(t1,t2) Sim being the similarity of the time attributes between alarmshost(host1,host2) Is a similarity value of the host attribute between alarms, simdesc*(desc1,desc2) Similar values of attributes are described for alarms from alarm to alarm.
simt(t1,t2) The calculation method of (2) is as follows:
simhost(host1,host2) The calculation method of (2) is a character string similarity method, comprising the following steps: a hamming distance, a jaro-willer distance, a jaccard distance, and the like, but is not limited thereto.
simdesc(desc1,desc2) The similarity of the description information between the alarms is mainly calculated. desc1Alert _ description, desc, of an alert _12For alert _ description of alert _2, after segmenting alert _ message, calculating the similarity between them by using a string similarity calculation method, including: a hamming distance, a jaro-willer distance, a jaccard distance, LCS, etc., but are not limited thereto.
The alarm alert _ input and the event incident are obtained by calculationiMaximum degree of similarity v _ maxi：v_maxi=max(v_sim1,v_sim2,...,v_simm)，
The alarm alert _ input and the event incident are obtained by calculationiAverage degree of similarity v meani：v_meani=mean(v_sim1,v_sim2,...,v_simm)。
And judging whether the alarm alert _ input is associated with the event or not or selecting not to associate the event according to the calculation result.
If the alarm alert _ input is associated with an event, the event's incedent _ last _ time is updated. The updating method is that if the alert _ time of the alert _ input is greater than the incident _ last _ time, the incident _ last _ time is updated to be the alert _ time.
If the alarm alert _ input cannot be associated with the event in the event list, a new event is created, the new event only includes the alarm alert _ input, and the incident _ last _ time of the new event is the alert _ time of the alarm.
In the method, in the face of the alarm storm of massive alarm messages, firstly, the massive alarm messages are subjected to alarm combination based on tree data structure search, and a large amount of repeated alarm information is combined into corresponding alarms; and secondly, calculating the similarity between the alarms by utilizing the semantic similarity, and associating the semantically adjacent alarms into an event. The operation and maintenance personnel can extract valuable alarm event information from events and effectively locate the system abnormity, thereby effectively processing alarm storm.
Fig. 6 is a schematic structural diagram illustrating an apparatus 60 for processing an alert message according to an embodiment of the present invention. As shown in fig. 6, the apparatus 60 includes:
an obtaining module 61, configured to obtain at least one alarm message;
a first merging module 62, configured to merge the warning messages by using a tree data structure to obtain at least one warning;
a calculation module 63 for calculating semantic similarity between the at least one alarm;
the second merging module 64 is configured to perform association processing on the at least one alarm according to semantic similarity between the at least one alarm, so as to obtain at least one event.
Optionally, merging the at least one alarm message by using a tree data structure to obtain at least one alarm, where the merging includes:
acquiring a merging parameter of an alarm message, wherein the merging parameter comprises an attribute of the alarm message and a first similarity threshold;
searching a tree data structure according to an attribute value list of the attribute of the alarm message in the merging parameter to obtain an alarm list stored by leaf nodes of the tree data structure; each layer of leaf node of the tree data structure stores an attribute value, and the attribute values of the alarm messages belonging to the same leaf node are the same;
calculating the similarity between the alarm message and the alarm list;
and combining the alarm messages according to the similarity between the alarm messages and the alarm list and the first similarity threshold value to obtain at least one alarm.
Optionally, merging the warning messages according to the similarity between the warning message and the warning list and the first similarity threshold, to obtain at least one warning, where the merging includes:
if the similarity between the alarm message and the alarm list is larger than the first similarity threshold, combining the alarm messages to obtain at least one alarm, and otherwise, creating a new alarm.
Optionally, the apparatus for processing an alarm message further includes a processing module, configured to perform at least one of the following:
if the occurrence time of the alarm message is longer than the occurrence time of the alarm, updating the occurrence time of the alarm to the occurrence time of the alarm message; the occurrence time of the alarm is the occurrence time of the alarm message combined last in the alarm;
and taking the description information of the alarm message and the longest common subsequence LCS of the description information of the alarm as the updated description information of the alarm.
Optionally, calculating a semantic similarity between the at least one alarm includes:
obtaining a correlation parameter of an alarm;
and calculating the maximum semantic similarity and the average semantic similarity of the target alarm and the effective events in the event list according to the correlation parameters.
Optionally, the associated parameters are: (T)window,Vmax_sim,Vmean_sim,Wt,Whost,Wdesc）；
Wherein, TwindowIs the validity window time of the event, Vmax_simIs the maximum threshold value, V, of event correlationmean_simIs an average threshold of event correlation, WtFor calculating temporal similarity weights when similarity between alarms, WhostFor the calculation of the host similarity weight in the similarity between alarms, WdescThe similarity weight of the description information of the alarm message when the similarity between the alarms is calculated;
calculating the maximum semantic similarity v _ max of the target alarm and the effective events in the event listiSimilarity with average semantic v meaniObtaining:
[(v_max1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]；
wherein n is the number of effective events in the event list;
taking the maximum value of the maximum semantic similarity list to obtain a corresponding first event, i.e. event _ a and maximum value v _ max:
incident_a,v_max=(v_max1,v_max2,...,v_maxn)；
taking the maximum value of the average semantic similarity list to obtain a corresponding second event incident _ b and a maximum value v _ mean:
incident_b,v_mean=(v_mean1,v_mean2,...,v_meann)。
optionally, performing association processing on the at least one alarm according to semantic similarity between the at least one alarm to obtain at least one event, where the association processing includes:
if V _ max > Vmax_simAdding the alarm into the first event, and updating the first event, to a;
if V _ max > Vmax_simNot satisfied, but satisfies V _ mean > Vmean_simAdding the alarm to the second event incident _ b and updating the second event incident _ b;
if V _ max > Vmax_simNot satisfied, V _ mean > Vmean_simIf not, then newly building aAnd adding the event into the event list.
It should be noted that this embodiment is an apparatus embodiment corresponding to the above method embodiment, and all the implementations in the above method embodiment are applicable to this apparatus embodiment, and the same technical effects can be achieved.
The embodiment of the invention provides a nonvolatile computer storage medium, wherein at least one executable instruction is stored in the computer storage medium, and the computer executable instruction can execute the processing method of the alarm message in any method embodiment.
Fig. 7 is a schematic structural diagram of a computing device according to an embodiment of the present invention, where the specific embodiment of the present invention does not limit a specific implementation of the computing device.
As shown in fig. 7, the computing device may include: a processor (processor), a Communications Interface (Communications Interface), a memory (memory), and a Communications bus.
Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers. The processor is used for executing a program, and particularly can execute relevant steps in the processing method embodiment of the alarm message for the computing device.
In particular, the program may include program code comprising computer operating instructions.
The processor may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program may specifically be configured to cause the processor to execute the method for processing an alert message in any of the method embodiments described above. For specific implementation of each step in the program, reference may be made to corresponding steps and corresponding descriptions in units in the above embodiments of the method for processing an alarm message, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best modes of embodiments of the invention.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. A method for processing an alarm message is characterized by comprising the following steps:
obtaining at least one warning message;
merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm;
calculating a semantic similarity between the at least one alert;
performing correlation processing on the at least one alarm according to semantic similarity between the at least one alarm to obtain at least one event;
wherein calculating semantic similarity between the at least one alert comprises:
obtaining a correlation parameter of an alarm;
calculating the maximum semantic similarity and the average semantic similarity of the target alarm and the effective events in the event list according to the correlation parameters;
wherein the correlation parameters are: (T)window,Vmax_sim,Vmean_sim,Wt,Whost,Wdesc）；
Wherein, TwindowIs the validity window time of the event, Vmax_simIs the maximum threshold value, V, of event correlationmean_simIs an average threshold of event correlation, WtFor calculating temporal similarity weights when similarity between alarms, WhostFor the calculation of the host similarity weight in the similarity between alarms, WdescThe similarity weight of the description information of the alarm message when the similarity between the alarms is calculated;
calculating the maximum semantic similarity v _ max of the target alarm and the effective events in the event listiSimilarity with average semantic v meaniObtaining:
[(v_max1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]；
wherein n is the number of effective events in the event list;
taking the maximum value of the maximum semantic similarity list to obtain a corresponding first event incident _ a and a maximum value v _ max:
incident_a,v_max=(v_max1,v_max2,...,v_maxn)；
taking the maximum value of the average semantic similarity list to obtain a corresponding second event incident _ b and a maximum value v _ mean:
incident_b,v_mean=(v_mean1,v_mean2,...,v_meann)。
2. the method of claim 1, wherein merging the at least one alert message using a tree data structure to obtain at least one alert comprises:
acquiring a merging parameter of an alarm message, wherein the merging parameter comprises an attribute of the alarm message and a first similarity threshold;
searching a tree data structure according to an attribute value list of the attribute of the alarm message in the merging parameters to obtain an alarm list stored by leaf nodes of the tree data structure; each layer of leaf node of the tree data structure stores an attribute value, and the attribute values of the alarm messages belonging to the same leaf node are the same;
calculating the similarity between the alarm message and the alarm list;
combining the alarm messages according to the similarity between the alarm messages and the alarm list and the first similarity threshold value to obtain at least one alarm;
wherein, according to the similarity between the alarm message and the alarm list and the first similarity threshold, merging the alarm messages to obtain at least one alarm, including:
if the similarity between the alarm message and the alarm list is larger than the first similarity threshold, combining the alarm messages to obtain at least one alarm, and otherwise, creating a new alarm.
3. The method for processing the alarm message according to claim 2, further comprising at least one of:
if the occurrence time of the alarm message is longer than the occurrence time of the alarm, updating the occurrence time of the alarm to the occurrence time of the alarm message; the occurrence time of the alarm is the occurrence time of the alarm message combined last in the alarm;
and taking the description information of the alarm message and the longest common subsequence LCS of the description information of the alarm as the updated description information of the alarm.
4. The method for processing an alert message according to claim 1, wherein associating the at least one alert according to semantic similarity between the at least one alert to obtain at least one event comprises:
if V _ max > Vmax_simAdding the alarm into the first event, and updating the first event, to a;
if V _ max > Vmax_simNot satisfied, but satisfies V _ mean > Vmean_simAdding the alarm to the second event incident _ b and updating the second event incident _ b;
if V _ max > Vmax_simNot satisfied, V _ mean > Vmean_simAnd if the condition is not met, an event is newly built and added into the event list.
5. An apparatus for processing an alert message, comprising:
the acquisition module is used for acquiring at least one alarm message;
the first merging module is used for merging the at least one alarm message by adopting a tree data structure to obtain at least one alarm;
a calculation module for calculating semantic similarity between the at least one alarm, wherein calculating semantic similarity between the at least one alarm comprises: obtaining a correlation parameter of an alarm, and calculating the maximum semantic similarity and the average semantic similarity of the target alarm and the effective events in the event list according to the correlation parameter, wherein the correlation parameter is as follows: (T)window,Vmax_sim,Vmean_sim,Wt,Whost,Wdesc) Wherein, TwindowIs the validity window time of the event, Vmax_simIs the maximum threshold value, V, of event correlationmean_simIs an average threshold of event correlation, WtFor calculating temporal similarity weights when similarity between alarms, WhostFor the calculation of the host similarity weight in the similarity between alarms, WdescCalculating the maximum semantic similarity v _ max of the target alarm and the effective events in the event list for the description information similarity weight of the alarm message when calculating the similarity between the alarmsiSimilarity with average semantic v meaniObtaining: [ (v _ max)1,v_mean1),(v_max2,v_mean2),...,(v_maxn,v_meann)]Wherein n is the number of effective events in the event list, and the maximum semantic similarity list is taken to obtain a corresponding first event incident _ a and a maximum v _ max:
incident_a,v_max=(v_max1,v_max2,...,v_maxn) Taking the maximum value of the average semantic similarity list to obtain a corresponding second event incident _ b and a maximum value v _ mean: incident _ b, v _ mean = (v _ mean)1,v_mean2,...,v_meann)；
And the second merging module is used for performing association processing on the at least one alarm according to the semantic similarity between the at least one alarm to obtain at least one event.
6. An electronic device, comprising: processor, memory storing a computer program which, when executed by the processor, performs the method of processing an alert message according to any one of claims 1 to 4.
7. A computer-readable storage medium for storing instructions which, when executed on a computer, cause the computer to perform the method of processing an alert message according to any one of claims 1 to 4.
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