CN111177311A - Data analysis model and analysis method of event processing result - Google Patents
Data analysis model and analysis method of event processing result Download PDFInfo
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- CN111177311A CN111177311A CN201911258027.XA CN201911258027A CN111177311A CN 111177311 A CN111177311 A CN 111177311A CN 201911258027 A CN201911258027 A CN 201911258027A CN 111177311 A CN111177311 A CN 111177311A
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- 238000012545 processing Methods 0.000 title claims abstract description 81
- 238000004458 analytical method Methods 0.000 title claims abstract description 22
- 238000007405 data analysis Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 32
- 230000008569 process Effects 0.000 claims abstract description 17
- 230000008878 coupling Effects 0.000 claims abstract description 15
- 238000010168 coupling process Methods 0.000 claims abstract description 15
- 238000005859 coupling reaction Methods 0.000 claims abstract description 15
- 238000005192 partition Methods 0.000 claims description 16
- 238000000638 solvent extraction Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 abstract description 15
- 230000001364 causal effect Effects 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the invention discloses a data analysis method of an event processing result, which comprises the following steps: step 100, abstracting an event set into a plurality of abstract bodies distributed according to a time axis according to a processing process, and setting event processing nodes on the time axis; step 200, setting a plurality of different coupling nodes on the whole event, and connecting the abstractions with the mutual relation to form a data body according to the coupling nodes; step 300, performing enumeration analysis on the cause of each data volume, and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis; step 400, further abstracting the relevant events in each data volume in a higher form according to the correlation coefficient; the invention also comprises a data analysis model, the invention carries out data on the complex events based on the abstract processing, thereby quickly screening out the target events, the detection range is wide, and the detection result comprises the causal relationship among different events.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing and analysis, in particular to a data analysis model and an analysis method of an event processing result.
Background
Complex event processing is a technology for extracting target information from a system based on distributed information, and the technology enables a user of the system to extract information required by the user from a large amount of information, wherein the information can be low-level network processing data or decision of a high-level enterprise manager. Whether the data is low-level or high-level is controlled by the operator of the system during the operation of the system and can be changed at any time.
However, in the existing data analysis of complex events, the events themselves are often directly analyzed, and the analysis has the following two defects:
firstly, the amount of information contained in an event is too much, so that a focus point of the event cannot be found, and the target information is difficult to be rapidly screened in limited computing resources;
second, the screening criteria for data in the existing method are determined, that is, screening based on the same criteria cannot determine different screening criteria according to different requirements, and cannot meet the requirements for processing complex event data under different realistic conditions.
Disclosure of Invention
Therefore, the embodiment of the invention provides a data analysis model and an analysis method of an event processing result, so as to solve the problems that the amount of information is large, the calculation processing is difficult, and the screening standard cannot be flexibly changed in the prior art.
In order to achieve the above object, an embodiment of the present invention provides the following:
a data analysis method of event processing results comprises the following steps:
step 100, abstracting an event set into a plurality of abstract bodies distributed according to a time axis according to a processing process, and setting event processing nodes on the time axis;
step 200, setting a plurality of different coupling nodes on the whole event, and connecting the abstractions with the mutual relation to form a data body according to the coupling nodes;
step 300, performing enumeration analysis on the cause of each data volume, and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis;
and step 400, further abstracting the relevant events in each data volume in a higher form according to the correlation coefficient.
As a preferred embodiment of the present invention, in step 100, the concrete steps of abstracting time into an abstract body are:
step 101, selecting an event set, and arranging events in sequence in the event set according to the distribution of a time axis;
102, partitioning the event set according to time periods, and identifying each event in each partition, wherein the identification principles are partition numbers, intra-partition sequence numbers and overall sequence numbers;
and 103, giving self attributes to each identified event for abstraction to form an abstract body.
As a preferred aspect of the present invention, the self-attribute includes a header file including time, place, related department and person, a summary file including cause, brief passage and result of the event, and a map file including detailed passage of the event.
As a preferred embodiment of the present invention, the step 100 further includes defining the nature of the event, and setting two processing modes according to the nature of the event, specifically including:
monitoring events, and processing and feeding back the time according to a predefined event type and a processing mode set corresponding to the event type;
and (4) customizing the time, and distributing the time to corresponding processors for processing and feedback by the creator according to the type and the property of the event.
As a preferred solution of the present invention, step 200 further includes constructing a connection between the abstractions, where the connection between the abstractions is determined according to the header file and the abstract file.
As a preferred embodiment of the present invention, the specific steps of matching the correlation coefficient of the event processing result in step 300 are as follows:
step 301, determining keywords which are mutually linked between abstract bodies, performing traversal search by using the keywords as characteristics of data body cause enumeration, and enumerating results of the traversal search in sequence;
step 302, determining a target word of an event processing result, and splitting the target word to obtain a plurality of independent contrast words;
step 303, selecting comparison words one by one to compare with all enumeration results one by one, recording comparison results according to identification numbers, and comparing comparison results of different comparison words again by taking the identification numbers as indexes;
and step 304, determining a threshold interval of index comparison, and determining different correlation coefficients according to different comparison results and the threshold interval.
In a preferred embodiment of the present invention, it is determined that the correlation coefficient is 0.8 or more.
As a preferred aspect of the present invention, in step 400, the concrete steps of further abstracting the event according to the correlation coefficient are:
step 401, determining a target event, and performing abstraction processing on the target event;
step 402, selecting keywords on the basis of abstraction processing, classifying the keywords, and matching the keywords with the determined correlation coefficients in the whole event according to the classified keywords;
and 403, determining a comparison result according to the matching result, and abstracting the comparison result to obtain a result.
In addition, the present invention provides a data analysis model of an event processing result, comprising:
the event sorting and abstracting module is used for abstracting the whole event set into a plurality of abstractions distributed according to a time axis according to the processing process, and setting event processing nodes on the time axis;
the coupling module is used for continuously forming the data body by the abstractions with the mutual relation according to the coupling nodes;
the enumeration module is used for performing enumeration analysis on the cause of each data body and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis;
and the comparison and abstraction module is used for further abstracting the related events in each data volume in a higher form according to the correlation coefficient.
As a preferred embodiment of the present invention, the sequencing and abstraction module of events further includes an event definition module, which is used to set different processing modes according to different properties of the events.
The embodiment of the invention has the following advantages:
the invention can detect the event of any abstraction layer, find out the target event in a series of events, can consider the messy information in the complex event according to the set correlation coefficient in the screening process to meet different requirements, abstract and summarize a series of events of a lower layer in the abstraction process, and perform preliminary screening and summarization on the complex event in the abstraction process, thereby facilitating the filing of subsequent event data, forming the event of a higher abstraction layer after abstraction, obtaining the event information in an application layer, and finding out the causal relationship among the events which occur in different time and different subsystems.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a schematic flow chart in an embodiment of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
The invention provides a data analysis model of an event processing result, which comprises the following steps:
the event sequencing and abstraction module is used for abstracting the whole event set into a plurality of abstractions distributed according to a time axis according to the processing process, setting event processing nodes on the time axis, and further comprises an event definition module used for setting different processing modes according to different event properties;
the coupling module is used for continuously forming the data body by the abstractions with the mutual relation according to the coupling nodes;
the enumeration module is used for performing enumeration analysis on the cause of each data body and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis;
and the comparison and abstraction module is used for further abstracting the related events in each data volume in a higher form according to the correlation coefficient.
In the invention, the data analysis model has the functions of screening target data from numerous disordered and scattered data events and performing traversal search according to nodes in the screening process, so that the screening efficiency can be improved. Due to the complexity of event data in actual operation, in order to guarantee the accuracy of data screening, a correlation coefficient is established to judge the threshold value. The above model is further described below in conjunction with data analysis methods.
The invention is based on the data analysis model, as shown in fig. 1, and further provides a data analysis method of an event processing result, which comprises the following steps:
step 100, abstracting the event set into a plurality of abstractions distributed according to a time axis according to a processing process, and setting an event processing node on the time axis.
In the present embodiment, the purpose of abstracting the set of events is to store complex time in a data format for subsequent processing. In the invention, for the analysis of complex time, the principle is also equivalent to converting a form which is inconvenient to pass through data processing into a data form so as to process the data.
In step 100, the concrete steps of abstracting time into an abstract body are as follows:
step 101, selecting an event set, and arranging events in sequence in the event set according to the distribution of a time axis, where it is to be noted that the premise that the event set is distributed according to the time axis is that the events occur in a time sequence, which includes times that occur simultaneously, but for events that do not occur in a time sequence, the events are defined in a manner of human intervention, and the events of this type will be further described in the following;
102, partitioning the event set according to time periods, and identifying each event in each partition, wherein the identification principles are partition numbers, intra-partition sequence numbers and overall sequence numbers;
and 103, giving self attributes to each identified event for abstraction to form an abstract body.
In step 102, in order to better explain the partitioning and identification processes, the following description is made with reference to an example:
for example, for 8 events occurring in chronological order, the events are respectively t1, t2, … …, t7 and t8 after being arranged according to a time axis, and are divided into three regions according to a partition rule, such as t1, t2 and t3 as regions S1, t4 and t5 as regions S2, and t6, t7 and t8 as regions S3.
Arranging and marking according to the sequence number of the partitioned intervals, namely recording the sequence numbers of different partitions as follows:
for the region S1, the sequence is S1-1, S1-2 and S1-3;
for the region S2, the sequence is marked as S2-1 and S2-2;
the sequence of the S3 region is S3-1, S3-2 and S3-3.
According to the partition rule, after the whole serial number is attached, the following steps are carried out in sequence: s1-1-1, S1-2-2, S1-3-3, S2-1-4, S2-2-5, S3-1-6, S3-2-7 and S3-3-8.
As described above, the identification principle of the partition number, the sequence number in the partition, and the whole sequence number is used to identify the partition number, the sequence number in the partition, and the whole sequence number to form a complete identification area, and after identification, the attribute of the identification area is attached to the identification area as the attached information.
The purpose of the processing mode is to completely convert the event into the data body, and the attribute of the event is contained in the identification area in an endowing mode, so that attribute information does not need to be considered in actual processing, and only in the actual traversing process, the attribute information needs to be considered, and therefore the data volume and the logical relationship in the operation process can be reduced.
In the above, the self-attributes include a header file containing time, place, related to department and person, a summary file containing the cause, brief passage and result of the event, and a map file containing the detailed passage of the event.
The header file and the abstract file comprise retrieval information of the event, basic information of the event can be obtained through the header file and the abstract file, so that the description of the event can be quickly and accurately obtained, traversal search is carried out based on the description of the event, and specific content is obtained through a mode of mapping the file after the search.
Step 100 further includes defining the nature of the event, and setting two processing modes according to the difference of the nature of the event, which specifically includes:
monitoring events, and processing and feeding back the time according to a predefined event type and a processing mode set corresponding to the event type;
and (4) customizing the time, and distributing the time to corresponding processors for processing and feedback by the creator according to the type and the property of the event.
Step 200, setting a plurality of different coupling nodes on the whole event, and connecting the abstractions with the mutual relation to form a data body according to the coupling nodes.
Step 200 also includes constructing the connection between the abstract bodies, wherein the connection between the abstract bodies is determined according to the header file and the abstract file.
It should be further explained that the coupling relationship in the present invention essentially places the events having mutual association together, so that the associated events can be rapidly obtained during traversal search, thereby reducing the repetitive traversal search work and improving the subsequent work efficiency.
And step 300, performing enumeration analysis on the cause of each data body, and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis.
Because different events have own attributes and it is difficult to directly determine which traversal condition the complex events belong to for the complex events, based on this consideration, the system can automatically determine by introducing a correlation coefficient in the embodiment, thereby improving the accuracy and efficiency of the final screening.
Further, by setting the correlation coefficient, a threshold value of the correlation coefficient can be set according to an actual demand, so that the screening can be performed according to the demand, and it is determined that the correlation coefficient is 0.8 or more as effective as in the present embodiment.
The specific steps of matching the event processing result correlation coefficients in step 300 are:
step 301, determining keywords which are mutually linked between abstract bodies, performing traversal search by using the keywords as characteristics of data body cause enumeration, and enumerating results of the traversal search in sequence;
step 302, determining a target word of an event processing result, and splitting the target word to obtain a plurality of independent contrast words;
step 303, selecting comparison words one by one to compare with all enumeration results one by one, recording comparison results according to identification numbers, and comparing comparison results of different comparison words again by taking the identification numbers as indexes;
and step 304, determining a threshold interval of index comparison, and determining different correlation coefficients according to different comparison results and the threshold interval.
In both step 301 and step 302, the complex event is subjected to a data processing, and after the data processing, a comparison is performed, and a correlation coefficient is determined according to a comparison result, so that a final result is obtained by performing abstraction on the comparison result in the subsequent step.
And step 400, further abstracting the relevant events in each data volume in a higher form according to the correlation coefficient.
In step 400, the concrete steps of further abstracting the event according to the correlation coefficient are as follows:
step 401, determining a target event, and performing abstraction processing on the target event;
step 402, selecting keywords on the basis of abstraction processing, classifying the keywords, and matching the keywords with the determined correlation coefficients in the whole event according to the classified keywords;
and 403, determining a comparison result according to the matching result, and abstracting the comparison result to obtain a result.
In the invention, the principle of data processing and analysis is as follows:
the actual complex event is abstracted into high-level time by means of data processing, so that the interference of disordered information in the screening process is avoided.
Based on the working mode, the method has the following advantages:
1. events of any abstract layer can be detected;
2. the method can find out the events which are interested by people, namely target events from a series of events;
3. a series of events at a lower layer can be abstracted and summarized, so that the interference of disordered information is avoided, and the abstract processing process is equivalent to primary screening and summarization of complex events, so that the subsequent event data can be conveniently filed; and after abstraction, events of a higher abstraction layer are formed, and event information can be obtained at an application layer.
4. Causal relationships between events occurring in different subsystems at different times can be found.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A data analysis method of event processing results is characterized by comprising the following steps:
step 100, abstracting an event set into a plurality of abstract bodies distributed according to a time axis according to a processing process, and setting event processing nodes on the time axis;
step 200, setting a plurality of different coupling nodes on the whole event, and connecting the abstractions with the mutual relation to form a data body according to the coupling nodes;
step 300, performing enumeration analysis on the cause of each data volume, and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis;
and step 400, further abstracting the relevant events in each data volume in a higher form according to the correlation coefficient.
2. The method for analyzing data of event processing result according to claim 1, wherein the concrete step of abstracting time into abstract body in step 100 is:
step 101, selecting an event set, and arranging events in sequence in the event set according to the distribution of a time axis;
102, partitioning the event set according to time periods, and identifying each event in each partition, wherein the identification principles are partition numbers, intra-partition sequence numbers and overall sequence numbers;
and 103, giving self attributes to each identified event for abstraction to form an abstract body.
3. The method of claim 2, wherein the self-attributes include a header file containing time, location, related department and person, a summary file containing the cause, brief passage and result of the event, and a mapping file containing the detailed passage of the event.
4. The method for analyzing data of event processing result according to claim 2, further comprising defining the property of the event in step 100, and setting two processing modes according to the difference of the property of the event, specifically comprising:
monitoring events, and processing and feeding back the time according to a predefined event type and a processing mode set corresponding to the event type;
and (4) customizing the time, and distributing the time to corresponding processors for processing and feedback by the creator according to the type and the property of the event.
5. The method for analyzing data of event processing result according to claim 1, further comprising constructing a connection between abstract bodies in step 200, wherein the connection between abstract bodies is determined according to the header file and the abstract file.
6. The method for analyzing event processing result data according to claim 1, wherein the step of matching the event processing result correlation coefficient in step 300 comprises the following steps:
step 301, determining keywords which are mutually linked between abstract bodies, performing traversal search by using the keywords as characteristics of data body cause enumeration, and enumerating results of the traversal search in sequence;
step 302, determining a target word of an event processing result, and splitting the target word to obtain a plurality of independent contrast words;
step 303, selecting comparison words one by one to compare with all enumeration results one by one, recording comparison results according to identification numbers, and comparing comparison results of different comparison words again by taking the identification numbers as indexes;
and step 304, determining a threshold interval of index comparison, and determining different correlation coefficients according to different comparison results and the threshold interval.
7. The method of claim 6, wherein the correlation coefficient is greater than or equal to 0.8, and the event processing result is determined to be valid.
8. The method for analyzing data of event processing results according to claim 6, wherein in step 400, the concrete steps of further abstracting the event according to the correlation coefficient are as follows:
step 401, determining a target event, and performing abstraction processing on the target event;
step 402, selecting keywords on the basis of abstraction processing, classifying the keywords, and matching the keywords with the determined correlation coefficients in the whole event according to the classified keywords;
and 403, determining a comparison result according to the matching result, and abstracting the comparison result to obtain a result.
9. A data analysis model of event processing results, comprising:
the event sorting and abstracting module is used for abstracting the whole event set into a plurality of abstractions distributed according to a time axis according to the processing process, and setting event processing nodes on the time axis;
the coupling module is used for continuously forming the data body by the abstractions with the mutual relation according to the coupling nodes;
the enumeration module is used for performing enumeration analysis on the cause of each data body and matching the correlation coefficient of the event processing result according to the result of the enumeration analysis;
and the comparison and abstraction module is used for further abstracting the related events in each data volume in a higher form according to the correlation coefficient.
10. The data analysis model of event processing results according to claim 9, further comprising an event definition module in the sequencing and abstraction module for setting different processing modes according to different event properties.
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