CN113535759A - Data labeling method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses a data labeling method, a data labeling device, data labeling equipment and a data labeling medium. The method comprises the following steps: capturing at least one first error message of a target system every other preset time period; calculating the similarity of each first error message and each second error message, wherein the second error messages are error messages crawled from a fault processing system for processing the fault of the target system; and marking first error information corresponding to the similarity larger than a preset similarity threshold. The data labeling method, device, equipment and medium provided by the embodiment of the invention can improve the accuracy of data labeling.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data annotation method, apparatus, device, and medium.
Background
With the continuous development of internet technology and computer science technology, artificial intelligence is becoming more and more intense. Artificial intelligence has developed to this day, and the role of data has become more and more prominent. The period, cost and accuracy of data annotation directly affect the industry competitiveness of an artificial intelligence company. The data marking is a behavior for processing the artificial intelligent learning data.
Currently, manual labeling is mainly adopted for labeling data. The accuracy of manually marking data is poor.
Disclosure of Invention
Embodiments of the present invention provide a data annotation method, apparatus, device, and medium, which can improve accuracy of data annotation.
In a first aspect, an embodiment of the present invention provides a data annotation method, including:
capturing at least one first error message of a target system every other preset time period;
calculating the similarity of each first error message and each second error message, wherein the second error messages are error messages crawled from a fault processing system for processing the fault of the target system;
and marking first error information corresponding to the similarity larger than a preset similarity threshold.
In some possible implementations of the embodiment of the present invention, capturing at least one first error message of the target system every preset time period includes:
and capturing at least one first error message of the target system by using the probe for capturing the error messages at intervals of a preset time period.
In some possible implementations of the embodiment of the present invention, before calculating the similarity between each first error message and each second error message, the data annotation method provided in the embodiment of the present invention further includes:
clustering at least one first error information.
In some possible implementations of the embodiment of the present invention, the data annotation method provided in the embodiment of the present invention further includes:
and marking the request time corresponding to the marked first error information.
In a second aspect, an embodiment of the present invention provides a data annotation device, including:
the system comprises a grabbing module, a processing module and a processing module, wherein the grabbing module is used for grabbing at least one first error message of a target system every preset time period;
the calculation module is used for calculating the similarity of each first error message and each second error message, wherein the second error messages are the error messages crawled from a fault processing system used for processing the target system fault;
and the marking module is used for marking the first error information corresponding to the similarity larger than the preset similarity threshold.
In some possible implementations of the embodiments of the present invention, the capture module is specifically configured to:
and capturing at least one first error message of the target system by using the probe for capturing the error messages at intervals of a preset time period.
In some possible implementations of the embodiment of the present invention, the data annotation device provided in the embodiment of the present invention further includes:
and the clustering module is used for clustering at least one first error message.
In some possible implementations of the embodiments of the present invention, the tagging module is further configured to:
and marking the request time corresponding to the marked first error information.
In a third aspect, an embodiment of the present invention provides a data annotation device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor;
the processor, when executing the computer program, implements the data backup method in the first aspect of the embodiments of the present invention or any possible implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the data annotation method in the first aspect of the embodiment or any possible implementation manner of the first aspect of the embodiment of the present invention.
The data marking method, the data marking device, the data marking equipment and the data marking medium can automatically mark data, improve the accuracy of data marking and improve the speed and efficiency of data marking compared with manual marking in the prior art.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data annotation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data annotation device according to an embodiment of the present invention;
fig. 3 is a block diagram of a hardware architecture of a computing device according to an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
When data marking is being done to current prior art, adopt artifical mode to mark one by one the full data on the one hand, consuming time and wasting power, it is relatively poor to influence marking efficiency and standard certainty greatly, and on the other hand adopts artifical mode to mark some data of screening, then utilizes machine learning to mark other data, has nevertheless promoted marking efficiency greatly, but has the problem that the mark is leaked or the mark accuracy can not be ensured. The method is used for marking the time series performance data of the huge data volume in an Internet Technology (IT) system, and the workload is large.
The data dimension of the IT system time sequence performance data includes quantized performance index data such as a request timestamp, response time, error times, slow request times, request times and the like, and does not include descriptive data such as specific error reasons or slow request reasons and the like, and whether the multidimensional data is abnormal at a certain moment can be calculated by an Artificial Intelligence (AI) algorithm, but the abnormality is only the abnormality output by the model, and whether the abnormality is real, an operation and maintenance person is required to label the abnormality. When the operation and maintenance personnel perform the labeling, whether the application at the time point is abnormal or not needs to be presumed according to whether the component where the IT system is located reports an error or whether the used resources are in shortage, so that the labeling difficulty is high, the efficiency is low, and the accuracy is low. Whether the IT system has an exception requires whether the request processed by the application system is wrong or not and the specific reason of the mistake is used for determining whether the application system has the exception or not.
Based on the above, embodiments of the present invention provide a data annotation method, apparatus, device, and medium. First, the data annotation method provided by the embodiment of the present invention is explained in detail below.
Fig. 1 is a schematic flowchart of a data annotation method according to an embodiment of the present invention. The data annotation method can comprise the following steps:
s101: and capturing at least one first error message of the target system every a preset time period.
S102: and calculating the similarity of each first error message and each second error message.
Wherein the second error information is the error information crawled from the fault handling system for handling the target system fault.
S103: and marking first error information corresponding to the similarity larger than a preset similarity threshold.
In some possible implementations of the embodiments of the present invention, the preset time period may be 1 second, 1 minute, or the like.
In some possible implementations of embodiments of the invention, the target system may be an IT system.
It is understood that the first error information refers to information generated when an error (such as a fault, an abnormality, etc.) occurs in the system.
In some possible implementations of embodiments of the invention, a probe for capturing error information may be deployed at each module of the target system, and the probe may capture error information of the error request.
It is understood that the probe in the embodiment of the present invention is a script program, and is a script file edited by using a programming language (e.g., JAVA language, ASP language, PHP language, etc.) to capture an error message.
In some possible implementations of embodiments of the present invention, a computer crawler may be utilized to crawl error information from a fault handling system for handling target system faults. The computer crawler is a program or script for automatically capturing required data according to a certain rule.
For example, assume that the captured error information of the target system is shown in table 1.
TABLE 1
| Error information |
| java.io.IOException:org.apache.axis2.AxisFault:Read timed out |
| SQLException:To exception:Connection timed out(Read failed) |
| …… |
The error information crawled from the fault handling system for handling the target system fault is shown in table 2.
TABLE 2
The similarity of each error information in table 1 and each error information in table 2 is calculated separately.
Assuming that the similarity between the error message "SQLEXCEPT": To EXCEPT ": Connection timed out (Read failed)" in Table 1 and the error message "SQLEXCEPT": To EXCEPT ": Connection timed out (Read failed)" in Table 2 is greater than the preset similarity threshold, the error message "SQLEXCEPT": To EXCEPT ": Connection timed out (Read failed)" is labeled.
In some possible implementations of embodiments of the present invention, the error message "SQLExlocation: To extent: Connection timeout out" may be marked as an abnormal error message.
The algorithm used for calculating the similarity between the first error message and the second error message is not limited in the present invention, and any available algorithm may be applied to the embodiments of the present invention. For example, cosine similarity, manhattan distance, euclidean distance, and mincemeat distance based on word vectors, edit distance, simhash, and number of common characters based on characters, and a jaccard similarity coefficient based on probability statistics, etc.
The similarity between the first error message and the second error message is calculated by using a simhash algorithm.
For the first error message "SQLEXCEPT" TO EXCEPTION "CONNECTION TIMED OUT (READ FAILED)", the first error message "SQLEXCEPT" TO EXCEPTION "CONNECTION TIMED OUT (READ FAILED)" is first participled and each word is given a weight.
The result of the word segmentation of "SQLException: To exception: Connection timed out (Read failed)" is: SQLException, To, exception, Connection, timed, out, Read, and failed.
The result of weighting each word is "SQLException (5), To (3), exception (4), Connection (4), timed (2), out (1), Read (2), failed (1)".
The hash value of each word is calculated using a hash function. The 32-bit 16-system hash value corresponding to the SQLException is: ad249e05eb304d96ade91e500634f62a, in binary notation "10101101001001001001111000000101111010110011000001001101100101101010110111101001000111100101000000000110001101001111011000101010".
And weighting each word on the basis of the hash value, wherein in the weighting process, 1 in the binary string is directly multiplied by the weight value by 1, and 0 is multiplied by the weight value by 1 and then is taken as negative. The weight vector corresponding to SQLException is [5, -5, 5, -5, 5, 5, 5, -5, … …, -5, -5, 5, -5. Similarly, weight vectors corresponding To "," exception "," Connection "," timed "," out "," Read ", and" failed ", respectively, can be obtained.
Adding the weight vectors respectively corresponding To the SQLEXceptance, To, exception, Connection, timed, out, Read and failed To obtain the weight vector corresponding To the first error information SQLEXceptance, To exception, Connection timed out, 6, 0, 9, 11, 12, 16, 20, assuming that the weight vectors are [18, -3, 6, 7, -6, 5, 7, -2, … …, -6, -1, 0, 9, 11, -12, 16, -20 ].
Setting the position more than 0 in the weight vector corresponding To the first error information SQLException To exception connecting timed out (Read failed) as 1, and setting the position less than or equal To 0 as 0, and obtaining the simhash value [1, 0, 1, 1, 0, … …, 0, 0, 1, 1, 0, 1, 0] corresponding To the first error information SQLException To exception connecting timed out (Read failed).
Similarly, the simhash values corresponding to the other first error messages and the second error messages respectively can be obtained.
Calculating the hamming distance between the simhash value corresponding To the first error information SQLException To exception Connection timeout out (Read failed) and the simhash value corresponding To each second error information.
Specifically, the simhash value corresponding To the first error message "SQLException: To exception: Connection timed out (Read failed)" is subjected To exclusive or operation with the simhash value corresponding To each second error message, and the number of 1 in the obtained result is the size of the hamming distance.
It is understood that the greater the hamming distance, the smaller the similarity.
For example, assuming that the preset hamming distance is 10, when the number of 1 s in the result obtained by performing xor operation on the simhash value corresponding to the first error message and the simhash value corresponding to the second error message is less than 10, it indicates that the similarity between the first error message and the second error message is greater than the preset similarity threshold. And marking the first error message.
The data marking method provided by the embodiment of the invention does not need manual marking, and can improve the accuracy, speed and efficiency of marking. In addition, the embodiment of the invention labels the full data, but not the screened partial data, so that the integrity of the label can be ensured.
In some possible implementations of the embodiment of the present invention, when at least one first error message of the target system is captured, a request time corresponding to each first error message may also be captured, and further, a request time corresponding to the first error message that has been labeled may be labeled.
For example, assume that the captured error information and request time of the target system are shown in table 3.
TABLE 3
Illustratively, it is assumed that the error message "SQLEXCEPT" TO EXCEPT "CONNECTION TIMED OUT (READ FAILED)" is marked, and then the error message "SQLEXCEPT" TO EXCEPT "CONNECTION TIMED OUT (READ FAILED)" corresponds To the request time "8: 31: 02/4/2/2019".
In some possible implementations of the embodiment of the present invention, the error information "SQLException: To exception: Connection timeout out (Read failed)" corresponding To the request time "8: 31:02 on 4/2/4/2019" may be marked as the abnormal time point.
In some possible implementations of the embodiment of the present invention, before calculating the similarity between each first error message and each second error message, at least one first error message may be further clustered.
For example, assume that the captured error information and request time of the target system are shown in table 4.
TABLE 4
The clustering results are shown in table 5.
TABLE 5
The similarity of each error information in table 5 and each error information in table 2 is calculated separately.
Assuming that the similarity between the error message "SQLEXCEPT: To EXCEPT: Connection timed out (Read failed)" in Table 5 and the error message "SQLEXCEPT: To EXCEPT: Connection timed out (Read failed)" in Table 2 is greater than the preset similarity threshold, the error message "SQLEXCEPT: To EXCEPT: Connection timed out (Read failed)" is labeled.
Further, the request time "8/2/4/2019/2/8/31/02/2019" corresponding To "SQLEXception: To exception: Connection timeout out (Read failed)" and "8/37/09/2019/4/2/8/09/2019" are labeled.
The process of calculating the similarity between each error message in table 5 and each error message in table 2 according to the embodiment of the present invention is similar to the process of calculating the similarity between each error message in table 1 and each error message in table 2, and specifically, the process of calculating the similarity between each error message in table 1 and each error message in table 2 may be referred to above. The embodiments of the present invention are not described herein in detail.
According to the data labeling method provided by the embodiment of the invention, the error information is clustered, so that the calculated amount of data labeling can be reduced, and the speed and efficiency of data labeling are improved.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a data annotation device.
Fig. 2 is a schematic structural diagram of a data annotation device according to an embodiment of the present invention. The data annotation device may include:
the capturing module 201 is configured to capture at least one first error message of the target system every preset time period;
a calculating module 202, configured to calculate a similarity between each first error message and each second error message, where the second error message is an error message crawled from a fault handling system for handling a fault of a target system;
the labeling module 203 is configured to label first error information corresponding to the similarity greater than a preset similarity threshold.
In some possible implementations of the embodiment of the present invention, the capturing module 201 may be specifically configured to:
and capturing at least one first error message of the target system by using the probe for capturing the error messages at intervals of a preset time period.
In some possible implementations of the embodiment of the present invention, the data annotation device provided in the embodiment of the present invention may further include:
and the clustering module is used for clustering at least one first error message.
In some possible implementations of the embodiment of the present invention, the labeling module 203 may further be configured to:
and marking the request time corresponding to the marked first error information.
For the embodiment of the data annotation device in the embodiment of the present invention, since it is basically similar to the embodiment of the data annotation method in the embodiment of the present invention, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the embodiment of the data annotation method in the embodiment of the present invention. The embodiments of the present invention are not described herein in detail.
The data marking device provided by the embodiment of the invention does not need manual marking, and can improve the accuracy, speed and efficiency of marking. In addition, the embodiment of the invention labels the full data, but not the screened partial data, so that the integrity of the label can be ensured.
The data marking device provided by the embodiment of the invention can improve the accuracy and the integrity of marking.
Fig. 3 is a block diagram of a hardware architecture of a computing device according to an embodiment of the present invention. As shown in fig. 3, computing device 300 includes an input device 301, an input interface 302, a central processor 303, a memory 304, an output interface 305, and an output device 306. The input interface 302, the central processing unit 303, the memory 304, and the output interface 305 are connected to each other through a bus 310, and the input device 301 and the output device 306 are connected to the bus 310 through the input interface 302 and the output interface 305, respectively, and further connected to other components of the computing device 300.
Specifically, the input device 301 receives input information from the outside and transmits the input information to the central processor 303 through the input interface 302; central processor 303 processes the input information based on computer-executable instructions stored in memory 304 to generate output information, stores the output information temporarily or permanently in memory 304, and then transmits the output information to output device 306 through output interface 305; output device 306 outputs the output information external to computing device 300 for use by the user.
That is, the computing device shown in FIG. 3 may also be implemented as a data annotation device, which may include: a memory storing a computer program; and a processor, which can implement the data annotation method provided by the embodiment of the invention when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium; the computer program realizes the data annotation method provided by the embodiment of the invention when being executed by a processor.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for annotating data, the method comprising:
capturing at least one first error message of a target system every other preset time period;
calculating the similarity of each first error message and each second error message, wherein the second error messages are error messages crawled from a fault processing system for processing the target system fault;
and marking first error information corresponding to the similarity larger than a preset similarity threshold.
2. The method of claim 1, wherein capturing at least one first error message of the target system every preset time period comprises:
and capturing at least one first error message of the target system by using the probe for capturing the error messages at intervals of a preset time period.
3. The method of claim 1, wherein before the calculating the similarity between each first error message and each second error message, the method further comprises:
clustering the at least one first error information.
4. The method according to any one of claims 1 to 3, further comprising:
and marking the request time corresponding to the marked first error information.
5. A data annotation device, said device comprising:
the system comprises a grabbing module, a processing module and a processing module, wherein the grabbing module is used for grabbing at least one first error message of a target system every preset time period;
the calculation module is used for calculating the similarity of each first error message and each second error message, wherein the second error messages are the error messages crawled from a fault processing system used for processing the target system fault;
and the marking module is used for marking the first error information corresponding to the similarity larger than the preset similarity threshold.
6. The device according to claim 5, wherein the grasping module is specifically configured to:
and capturing at least one first error message of the target system by using the probe for capturing the error messages at intervals of a preset time period.
7. The apparatus of claim 5, further comprising:
and the clustering module is used for clustering the at least one piece of first error information.
8. The apparatus of any one of claims 5 to 7, wherein the labeling module is further configured to:
and marking the request time corresponding to the marked first error information.
9. A data annotation apparatus, characterized in that said apparatus comprises: a memory, a processor, and a computer program stored on the memory and executable on the processor;
the processor, when executing the computer program, implements the data annotation method of any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, implements the data annotation method according to any one of claims 1 to 4.
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Application publication date: 20211022 |