A data alarm analysis method and device

Abstract

The present application discloses a data alarm analysis method and device, which includes: obtaining alarm information from a database at preset intervals; determining whether there are important keywords in the alarm information; if there are no important keywords in the alarm information, respectively calculating the similarity between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base; determining each target knowledge from each piece of knowledge based on the similarity; wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold; if there are important keywords in the alarm information; searching the knowledge base for each target knowledge with the same important keywords; and outputting access links corresponding to each target knowledge. Thus, instead of manually searching the knowledge base for content associated with the alarm information, the corresponding knowledge content is automatically searched from the knowledge base based on the keywords in the alarm information, thereby effectively improving the efficiency of the search.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for analyzing data alarms. Background Art

[0002] For enterprise development, many companies will introduce multiple databases when using domestic database products. However, for database administrators, in addition to maintaining the existing Oracle database, they also have to deal with multiple other databases. In the process of database operation and maintenance, they may also need to face various problems caused by multiple data. Therefore, how to efficiently analyze problems has become an issue that database administrators urgently need to face and solve.

[0003] In the existing technology, a knowledge base is mainly established in advance, which contains various database knowledge. When an alarm message appears in the database, the staff searches the knowledge base for knowledge content related to the alarm message based on their own experience and knowledge, and provides feedback so that the database administrator can analyze the alarm content based on the knowledge content.

[0004] Since the content related to the alarm information is searched from the knowledge base manually, the efficiency is relatively low and errors are prone to occur during the search process. Summary of the Invention

[0005] Based on the above-mentioned deficiencies of the prior art, the present application provides a data alarm analysis method and device to solve the problem of low efficiency of the manual search method in the prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The first aspect of the present application provides a data alarm analysis method, comprising:

[0008] Obtain alarm information from the database at preset intervals;

[0009] Determine whether there are important keywords in the alarm information; wherein the important keywords refer to keywords carrying numbers;

[0010] If the important keyword does not exist in the warning information, then respectively calculating the similarity between the warning information and the non-important keyword corresponding to each piece of knowledge in the knowledge base;

[0011] Determining each target knowledge from each piece of knowledge according to each similarity; wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold;

[0012] If the important keyword exists in the warning information, searching for each target knowledge item that is the same as the important keyword from the knowledge base;

[0013] Output the access link corresponding to each target knowledge.

[0014] Optionally, in the above-mentioned analysis method based on alarms, before respectively calculating the similarity between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base, the method further includes:

[0015] Extracting English keywords corresponding to the alarm information from the alarm information;

[0016] Deleting the English keyword from the warning information;

[0017] The deleted keywords in the warning information are segmented using separators.

[0018] Optionally, in the above-mentioned analysis method based on alarms, respectively calculating the similarity between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base includes:

[0019] For each piece of knowledge in the knowledge base, detecting whether there is an intersection between the warning information and the non-important keywords corresponding to the knowledge;

[0020] If there is an intersection between the warning information and the non-important keywords corresponding to the knowledge, obtaining the intersection between the warning information and the non-important keywords corresponding to the knowledge;

[0021] Merging the warning information with non-important keywords corresponding to the knowledge to obtain a union;

[0022] Dividing the intersection by the union to obtain the similarity between the warning information and the non-important keywords corresponding to the knowledge;

[0023] If there is no intersection between the warning information and the non-important keywords corresponding to the knowledge, it is determined that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero.

[0024] Optionally, in the above-mentioned analysis method based on an alarm, determining each target knowledge item from each piece of knowledge item according to each similarity includes:

[0025] Searching for each target similarity greater than a preset threshold from the similarities between the warning information and the non-important keywords corresponding to each piece of knowledge;

[0026] The knowledge corresponding to each target similarity is respectively determined as target knowledge to obtain multiple pieces of target knowledge.

[0027] Optionally, the above-mentioned alarm analysis method further includes:

[0028] Obtaining target alarm information from a knowledge base; wherein the target alarm information refers to information newly registered or modified by a database administrator;

[0029] Performing word segmentation processing on the target warning information;

[0030] Extracting each keyword of the target warning information from the target warning information after word segmentation;

[0031] If there is a target keyword with a number among the keywords, the target keyword is updated to the important keyword queue;

[0032] If each of the keywords does not carry a number, each of the keywords is updated to a non-important keyword queue.

[0033] Optionally, in the above-mentioned analysis method based on an alarm, the outputting of the access link corresponding to the target knowledge includes:

[0034] If the similarity between each target knowledge item and the non-important keyword is greater than a preset threshold, sort the target knowledge items in descending order according to the similarity between each target knowledge item and the non-important keyword;

[0035] If each of the target knowledge items refers to the same knowledge as the important keyword, then sort the target knowledge items in descending order according to their system names and value scores;

[0036] According to the descending order of each target knowledge, the access link corresponding to each target knowledge is output in sequence.

[0037] Optionally, the above-mentioned alarm analysis method further includes:

[0038] Obtaining problem management scores corresponding to multiple database administrators based on monthly, quarterly, or annual reading times; wherein the problem management scores refer to scores of the database administrators for solving the problems registered by themselves;

[0039] Counting the problem management scores corresponding to the database administrators to obtain the total problem management scores corresponding to the database administrators;

[0040] The database administrators are ranked according to their corresponding total problem management scores.

[0041] Optionally, the above-mentioned alarm analysis method further includes:

[0042] When an alarm message is detected in the database, the corresponding database administrator is searched according to the host name of the database;

[0043] Feedback prompt information to the database administrator; wherein the prompt information is used to prompt the database administrator to analyze and register the alarm information existing in the database.

[0044] A second aspect of the present application provides a data alarm analysis device, comprising:

[0045] An information acquisition unit, configured to acquire alarm information from a database at preset intervals;

[0046] A judging unit, configured to judge whether an important keyword exists in the warning information; wherein the important keyword refers to a keyword carrying a number;

[0047] a similarity calculation unit, configured to calculate the similarity between the warning information and the non-important keywords corresponding to each piece of knowledge in the knowledge base if the important keyword does not exist in the warning information;

[0048] A knowledge determination unit, configured to determine each target knowledge from each piece of knowledge according to each similarity; wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold;

[0049] a knowledge search unit, configured to search for target knowledge items identical to the important keywords from a knowledge base if the important keywords exist in the warning information;

[0050] The link output unit is used to output the access link corresponding to each target knowledge.

[0051] Optionally, the above-mentioned alarm analysis device further includes:

[0052] A first extraction unit is used to extract English keywords corresponding to the alarm information from the alarm information;

[0053] a deleting unit, configured to delete the English keyword from the warning information;

[0054] The word segmentation unit is used to perform word segmentation processing on the keywords in the deleted alarm information using separators.

[0055] Optionally, in the above-mentioned alarm analysis device, the similarity calculation unit includes:

[0056] a detection unit, configured to detect, for each piece of knowledge in the knowledge base, whether there is an intersection between the warning information and the non-important keywords corresponding to the knowledge;

[0057] an intersection obtaining unit, configured to obtain the intersection of the warning information and the non-important keywords corresponding to the knowledge if there is an intersection between the warning information and the non-important keywords corresponding to the knowledge;

[0058] a merging unit, configured to merge the warning information with non-important keywords corresponding to the knowledge to obtain a union;

[0059] a calculation unit, configured to divide the intersection by the union to obtain a similarity between the warning information and the non-important keywords corresponding to the knowledge;

[0060] The first determining unit is configured to determine that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero if there is no intersection between the warning information and the non-important keywords corresponding to the knowledge.

[0061] Optionally, in the above-mentioned alarm analysis device, the knowledge determination unit includes:

[0062] A similarity search unit, configured to search for target similarities greater than a preset threshold from the similarities between the warning information and the non-important keywords corresponding to each piece of knowledge;

[0063] The second determining unit is used to determine the knowledge corresponding to each target similarity as target knowledge, and obtain multiple pieces of target knowledge.

[0064] Optionally, the above-mentioned alarm analysis device further includes:

[0065] An acquisition unit, configured to acquire target alarm information from a knowledge base; wherein the target alarm information refers to information newly registered or modified by a database administrator;

[0066] a processing unit, configured to perform word segmentation processing on the target warning information;

[0067] A second extraction unit is used to extract each keyword of the target warning information from the target warning information after word segmentation;

[0068] A first updating unit is configured to update a target keyword with a number to an important keyword queue if the target keyword is included in each of the keywords;

[0069] The second updating unit is configured to update each of the keywords to a non-important keyword queue if the keywords do not carry a number.

[0070] Optionally, in the above-mentioned alarm analysis device, the link output unit includes:

[0071] a first sorting unit, configured to sort each of the target knowledge items in descending order according to the similarity of the non-important keywords corresponding to each of the target knowledge items, if the similarity of each of the target knowledge items to the non-important keywords is greater than a preset threshold;

[0072] a second sorting unit, configured to sort each of the target knowledge items in descending order according to the system name and value score of each of the target knowledge items if each of the target knowledge items refers to the same knowledge as the important keyword;

[0073] The output unit is used to output the access links corresponding to the target knowledge in descending order of the target knowledge.

[0074] Optionally, the above-mentioned alarm analysis device further includes:

[0075] A score acquisition unit is used to acquire problem management scores corresponding to multiple database administrators according to monthly, quarterly or annual reading time; wherein the problem management score refers to the score of the database administrator for solving the problem registered by the database administrator;

[0076] a statistical unit, configured to collect statistics on the problem management scores corresponding to the database administrators to obtain a total problem management score corresponding to the database administrators;

[0077] The ranking unit is used to rank each of the database administrators according to the total problem management score corresponding to each of the database administrators.

[0078] Optionally, the above-mentioned alarm analysis device further includes:

[0079] A search unit, configured to search for a corresponding database administrator according to a host name of the database when an alarm message is detected in the database;

[0080] A feedback unit is used to feed back prompt information to the database administrator; wherein the prompt information is used to prompt the database administrator to analyze and register the alarm information existing in the database.

[0081] The present application provides a method for analyzing data alarms, which obtains alarm information from a database at preset intervals, and then determines whether there are important keywords in the alarm information, wherein important keywords refer to keywords with numbers. If there are no important keywords in the alarm information, the similarities between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base are calculated respectively. Based on each similarity, each target knowledge is determined from each piece of knowledge, wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold. If there are important keywords in the alarm information, each target knowledge with the same important keywords is searched from the knowledge base, and finally the access link corresponding to each target knowledge is output. Therefore, it is no longer necessary to manually search for content related to the alarm information from the knowledge base, but to automatically search for corresponding knowledge content from the knowledge base based on the keywords in the alarm information, thereby effectively improving the efficiency of the search. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0083] Figure 1 A flowchart of a database operation and maintenance management platform is provided for an embodiment of the present application;

[0084] Figure 2 A flowchart of a data alarm analysis method provided in an embodiment of the present application;

[0085] Figure 3 A flowchart of a method for pre-processing alarm information provided in an embodiment of the present application;

[0086] Figure 4 A flowchart of a similarity calculation method provided in an embodiment of the present application;

[0087] Figure 5 A flowchart of a method for searching for target knowledge is provided for an embodiment of the present application;

[0088] Figure 6 A flowchart of a performance appraisal method provided in an embodiment of the present application;

[0089] Figure 7 A flowchart of a keyword queue updating method provided in an embodiment of the present application;

[0090] Figure 8 A structural diagram of a data alarm analysis device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0091] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0092] In this application, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0093] The embodiment of the present application provides a data alarm analysis method to solve the problem of low efficiency of the existing manual search method.

[0094] Optionally, the data alarm analysis method provided in the embodiment of the present application can be applied to a database operation and maintenance management platform. Therefore, in order to implement the data alarm analysis method provided in the embodiment of the present application, the embodiment of the present application optionally provides a database operation and maintenance management platform, such as Figure 1 As shown, it includes: database alarm management function, database information and division of labor function, database knowledge management function, performance appraisal function and alarm-related knowledge reference function.

[0095] It should be noted that the database alarm management function is mainly used to regularly extract database alarm information from the event management platform based on alarm rules such as alarm level and alarm category.

[0096] The database information and labor division feature is primarily used to assign each database and its corresponding database information to the appropriate database administrator. This information can include system name, host name, IP address, deployment mode, database name, port number, DBA, system manager, and more. Furthermore, through this functionality, each alarm can be assigned to a specific database administrator based on the host name, giving priority to the database administrator for alarm analysis and knowledge registration.

[0097] The database knowledge management function is mainly used to manage the registrant, registration date, system name, host name, problem description, problem analysis process, solution, solution progress, value points, important keywords (the system automatically extracts them from the "problem description" without manual registration), and non-important keywords (the system automatically extracts them from the "problem description" without manual registration). Specifically, the database administrator can register the analyzed problem in the database knowledge management function through his or her own account, and fill in relevant information except the registrant (automatically identified) and value points. Among them, after the registration is completed, the value points will be scored by the team leader based on factors such as the situation of the alarm information resolution, the difficulty level, and the degree of resolution, with a score of 1-10 points. Among them, the value points refer to the score of the knowledge solution alarm information.

[0098] The performance appraisal function is mainly used to compile statistics and rank the monthly / quarterly / annual problem management scores of database administrators.

[0099] The alarm-related knowledge reference function is mainly used to analyze and match the alarm information extracted from the database with the knowledge base, find matching knowledge, and then provide access links to related knowledge on the alarm detail page.

[0100] Based on the above database operation and maintenance management platform, the embodiment of the present application provides a data alarm analysis method, such as Figure 2 As shown, specifically including:

[0101] S201. Obtain alarm information from a database at preset intervals.

[0102] Specifically, in order to narrow the scope of database alarms that the database administrator is concerned about, thereby improving the efficiency of alarm handling, in an embodiment of the present application, database alarm information is obtained from the event management platform every minute based on the alarm level and alarm category.

[0103] Optionally, the preset time may be one minute, or other thresholds, and may be specifically set according to needs.

[0104] Among them, the alarm levels include: major alarm and minor alarm.

[0105] Alarm categories include: LINUX, WINDOWX, AIX, HPUX, server, network, Oracle, MySQL, TiDB, DM, GDB and other database alarms.

[0106] Optionally, in order to allow a database administrator to promptly be informed of the presence of alarm information in the database and to promptly process the alarm information, before executing step S201, an embodiment of the present application provides a method for prompting alarm information, comprising the following steps:

[0107] When an alarm message is detected in the database, the corresponding database administrator is found based on the database host name.

[0108] It should be noted that in the embodiment of the present application, each database has a corresponding database administrator in charge, and the database administrator will be bound to the host name of the database, so you only need to find the corresponding database administrator based on the host name of the database.

[0109] Feedback prompt information to the database administrator.

[0110] The prompt information is used to prompt the database administrator to analyze and register the alarm information existing in the database.

[0111] Specifically, in order for the database administrator to analyze and register the database alarm information in a timely manner, it is necessary to provide the database administrator with prompt information in advance. The database administrator then only needs to wait for the system to find the knowledge that best matches the alarm information to resolve the alarm information. In this process, the database administrator needs to register the alarm information in the knowledge base.

[0112] S202: Determine whether there are important keywords in the alarm information.

[0113] Among them, important keywords refer to keywords that carry numbers. For example, important keywords are expressed in the form of: ORA-xxxxx, MY-xxxxx, DM-xxxxxx, etc. It should be noted that the alarm information contains many keywords, and the knowledge base contains various database knowledge. The corresponding knowledge can be found from the knowledge base based on important keywords to solve the alarm information. Therefore, the alarm information needs to be further judged, that is, it can be judged whether there are important keywords in the alarm information based on the queue of important keywords. If there are no important keywords in the alarm information, step S203 needs to be executed. If there are important keywords in the alarm information, step S205 is executed. Among them, the important keyword queue refers to a queue that only contains important keywords.

[0114] S203: Calculate the similarity between the warning information and the non-important keywords corresponding to each piece of knowledge in the knowledge base.

[0115] Specifically, when the alarm message does not contain important keywords, the knowledge base can be used to determine the pieces of knowledge that resolve the alarm message based on the similarity between the keywords in the alarm message and the non-important keywords corresponding to each piece of knowledge in the knowledge base. This requires precalculating the similarity between the alarm message and the non-important keywords corresponding to each piece of knowledge in the knowledge base. The present embodiment uses the Jaccard coefficient calculation method, but is certainly not limited to this similarity calculation method.

[0116] Optionally, since there are many keywords in the warning information, in order to quickly and easily calculate the similarity between the keywords in the warning information and the non-important keywords, the warning information can be segmented in advance before executing step S203. Figure 3 As shown, the embodiment of the present application provides a method for pre-processing alarm information, comprising the following steps:

[0117] S301: Extract English keywords corresponding to the alarm information from the alarm information.

[0118] Specifically, there may be some commonly used English keywords (for example, is, the, etc.) in the alarm information, which do not have much effect on the subsequent similarity calculation and may also affect the efficiency of the system word segmentation. Therefore, it is necessary to extract the English keywords corresponding to the alarm information from the alarm information in advance.

[0119] S302: Delete English keywords from the warning information.

[0120] Specifically, step S302 is performed to prevent the system from affecting word segmentation efficiency due to unnecessary English keywords, and also to reduce the interference of some unnecessary characters. Therefore, English keywords (eg, am, he, etc.) need to be deleted from the alarm information in advance.

[0121] S303: Use separators to perform word segmentation on the keywords in the deleted alarm information.

[0122] It should be noted that in order to conveniently and quickly calculate the similarity between the keywords in the alarm information and non-important keywords, and to prevent the similarity from being affected by the inability to distinguish the keywords, in an embodiment of the present application, a separator is used to segment the keywords in the deleted alarm information, so that a well-segmented alarm information can be obtained.

[0123] Optionally, in another embodiment of the present application, a specific implementation of step S203 is as follows: Figure 4 As shown, the following steps are included:

[0124] S401 : For each piece of knowledge in the knowledge base, detect whether there is an intersection between the alarm information and the non-important keywords corresponding to the knowledge.

[0125] It should be noted that the degree of similarity depends on whether the alarm information and the knowledge contain the same keywords. Therefore, in the embodiment of the present application, it is necessary to first detect whether there is an intersection between the alarm information and the non-important keywords corresponding to the knowledge. If there is an intersection between the alarm information and the non-important keywords corresponding to the knowledge, it means that the alarm information and the knowledge contain the same keywords, so step S402 needs to be executed. If there is no intersection between the alarm information and the non-important keywords corresponding to the knowledge, it means that the alarm information and the knowledge do not contain the same keywords, so step S405 is executed.

[0126] S402: Obtain an intersection between the warning information and non-important keywords corresponding to the knowledge.

[0127] Specifically, when it is detected that there is an intersection between the alarm information and the non-important keywords corresponding to the knowledge, the target keywords that are the same as the non-important keywords corresponding to the knowledge can be extracted from the alarm information, and then the target keywords can be integrated to obtain the intersection of the alarm information and the non-important keywords corresponding to the knowledge.

[0128] S403: Merge the warning information and the non-important keywords corresponding to the knowledge to obtain a union.

[0129] Specifically, in the embodiment of the present application, the Jaccard coefficient calculation method is adopted. In the process of calculating the similarity, the union of the alarm information and the non-important keywords corresponding to the knowledge is required for calculation, so step S403 needs to be executed.

[0130] S404: Divide the intersection by the union to obtain the similarity between the warning information and the non-important keywords corresponding to the knowledge.

[0131] S405: Determine that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero.

[0132] Specifically, when it is detected that there is no intersection between the alarm information and the non-important keywords corresponding to the knowledge, it means that there are no identical keywords in the alarm information and the knowledge, and thus it can be determined that the similarity between the alarm information and the non-important keywords corresponding to the knowledge is zero.

[0133] S204: Determine each target knowledge item from each piece of knowledge according to each similarity.

[0134] Among them, the similarity of non-important keywords corresponding to the target knowledge is greater than a preset threshold.

[0135] It should be noted that the higher the similarity between the alarm information and the non-important keywords corresponding to the knowledge, the closer the association between this knowledge and the alarm information, and it is very likely that the alarm information in the database can be solved with this knowledge. Therefore, in the embodiment of the present application, it is necessary to select from each piece of knowledge the target knowledge whose similarity of non-important keywords is greater than the preset threshold, and provide it to the database administrator for reference.

[0136] Optionally, the preset threshold may be 0.33, or other thresholds, which may be set specifically according to requirements.

[0137] Optionally, in another embodiment of the present application, a specific implementation of step S204 is as follows: Figure 5 As shown, the following steps are included:

[0138] S501 : searching for target similarities greater than a preset threshold from the similarities between the warning information and the non-important keywords corresponding to each piece of knowledge.

[0139] It should be noted that after obtaining the similarity between the alarm information and the non-important keywords corresponding to each piece of knowledge, it is necessary to find the target similarities that are greater than the preset threshold. Because the knowledge corresponding to the target similarity that is greater than the preset threshold can represent the content that is closely related to the alarm information, it can enable database administrators to effectively solve the database alarm information, improve the efficiency of alarm handling, and reduce operation and maintenance risks.

[0140] S502: Determine the knowledge corresponding to each target similarity as target knowledge, and obtain multiple target knowledge.

[0141] S205. Search the knowledge base for each piece of target knowledge that is identical to the important keyword.

[0142] Specifically, when there are important keywords in the alarm information, various pieces of target knowledge that are the same as the important keywords can be directly searched and provided to the database administrator.

[0143] S206: Output the access link corresponding to each piece of target knowledge.

[0144] Optionally, an access link corresponding to each target knowledge item may be output to the database administrator on the detailed page of the alarm, and the database administrator may then process the alarm information in a timely manner based on the knowledge in the access link.

[0145] Optionally, in another embodiment of the present application, a specific implementation of step S206 includes the following steps:

[0146] If the similarity between each target knowledge item and the non-important keyword is greater than a preset threshold, the target knowledge items are sorted in descending order according to the similarity between the non-important keywords corresponding to each target knowledge item.

[0147] Specifically, when the similarity of each target knowledge item to a non-important keyword is greater than a preset threshold, in order to let the database administrator know which target knowledge items are closely related to the alarm information and can solve the alarm information of the database to a large extent, in an embodiment of the present application, each target knowledge item will be sorted in descending order according to the similarity corresponding to the target knowledge item.

[0148] If each piece of target knowledge refers to the same knowledge as the important keyword, then each piece of target knowledge is sorted in descending order according to its system name and value score.

[0149] Specifically, if each piece of target knowledge refers to the same knowledge as an important keyword, in order to let the database administrator know which pieces of target knowledge can provide the greatest value for solving the database alarm information, in an embodiment of the present application, each piece of target knowledge will be sorted in descending order according to the system name and value score of each piece of target knowledge.

[0150] Output the access links corresponding to each target knowledge item in descending order.

[0151] Optionally, only the top N pieces of target knowledge may be output in descending order of the target knowledge.

[0152] Optionally, in order to motivate the database administrator to analyze or register the daily handling of the database or the problems encountered, such as Figure 6 As shown, the embodiment of the present application provides a performance evaluation method, including the following steps:

[0153] S601: Obtain corresponding problem management scores of multiple database administrators according to monthly, quarterly, or annual reading time.

[0154] The problem management score refers to the score that the database administrator scores for solving the problems registered by himself.

[0155] It should be noted that to prevent database administrators from failing to promptly process or analyze alarm information, performance evaluations are conducted at the end of each month, quarter, or year. Database administrators who fail to meet performance targets are penalized, while those who meet them are rewarded. Therefore, in this application, problem management scores for multiple database administrators are obtained based on the monthly, quarterly, or annual read time. The problem management scores for database administrators are assigned by the team leader based on their resolution of alarm information.

[0156] S602: Count the problem management scores corresponding to the database administrators to obtain the total problem management scores corresponding to the database administrators.

[0157] Specifically, in order to facilitate the understanding of the monthly, quarterly or annual ranking of each database administrator, the corresponding monthly, quarterly or annual problem management scores of each database administrator will be counted in advance to obtain the corresponding total problem management score of each database administrator.

[0158] S603: Rank each database administrator according to their corresponding total problem management score.

[0159] Optionally, the top N database administrators may be rewarded and the bottom M database administrators may be punished based on the total scores.

[0160] Optionally, in order to update important keywords and non-important keywords in a timely manner and improve search efficiency, the embodiment of the present application provides a method for updating a keyword queue, such as Figure 7 As shown, the following steps are included:

[0161] S701. Obtain target alarm information from a knowledge base.

[0162] The target alarm information refers to the information newly registered or modified by the database administrator.

[0163] S702: Perform word segmentation processing on the target warning information.

[0164] Specifically, in order to easily identify whether there are important keywords in the target warning information, it is necessary to perform word segmentation processing on the target warning information in advance.

[0165] S703: Extract keywords of the target warning information from the segmented target warning information.

[0166] It should be noted that in addition to keywords, the target alarm information may also contain other information. Therefore, in order to be able to flexibly and efficiently update each keyword to the important keyword queue or non-important keyword queue in the future, the keywords of the target alarm information are extracted in advance from the target alarm information after word segmentation.

[0167] S704: Determine whether there is a target keyword with a number among the keywords.

[0168] It should be noted that important keywords are numbered keywords, so it is only necessary to determine whether there is a numbered target keyword among the keywords to effectively distinguish the keywords. Therefore, if there is a numbered target keyword among the keywords, step S705 is executed. If there is no number among the keywords, step S706 is executed.

[0169] S705: Update the target keyword to the important keyword queue.

[0170] Specifically, when there is a target keyword with a number among the keywords, and the target keyword has not existed in the important keyword queue, the target keyword is updated to the important keyword queue to avoid repeated storage.

[0171] S706: Update each keyword to the non-important keyword queue.

[0172] Specifically, when each keyword does not carry a number and each keyword has not existed in the important keyword queue, each keyword is updated to the non-important keyword queue.

[0173] The present application provides a method for analyzing data alarms, which obtains alarm information from a database at preset intervals, and then determines whether there are important keywords in the alarm information, wherein important keywords refer to keywords with numbers. If there are no important keywords in the alarm information, the similarities between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base are calculated respectively. Based on each similarity, each target knowledge is determined from each piece of knowledge, wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold. If there are important keywords in the alarm information, each target knowledge with the same important keywords is searched from the knowledge base, and finally the access link corresponding to each target knowledge is output. Therefore, it is no longer necessary to manually search for content related to the alarm information from the knowledge base, but to automatically search for corresponding knowledge content from the knowledge base based on the keywords in the alarm information, thereby effectively improving the efficiency of the search.

[0174] Another embodiment of the present application provides a data alarm analysis device, such as Figure 8 As shown, it includes the following units:

[0175] The information acquisition unit 801 is configured to acquire alarm information from a database at preset intervals.

[0176] The judging unit 802 is configured to judge whether there are important keywords in the warning information.

[0177] Among them, important keywords refer to keywords carrying numbers.

[0178] The similarity calculation unit 803 is configured to calculate the similarity between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base if there are no important keywords in the alarm information.

[0179] The knowledge determination unit 804 is configured to determine each target knowledge item from each piece of knowledge according to each similarity.

[0180] Among them, the similarity of non-important keywords corresponding to the target knowledge is greater than a preset threshold.

[0181] The knowledge search unit 805 is configured to search for target knowledge items that are identical to the important keywords in the knowledge base if the alarm information contains the important keywords.

[0182] The link output unit 806 is used to output the access link corresponding to each target knowledge item.

[0183] It should be noted that the specific working process of the above-mentioned units in the embodiment of the present application can refer to steps S201 to S206 in the above-mentioned method embodiment, and will not be repeated here.

[0184] Optionally, another embodiment of the present application provides a data alarm analysis device, further comprising:

[0185] The first extraction unit is configured to extract English keywords corresponding to the alarm information from the alarm information.

[0186] The deletion unit is used to delete English keywords from the alarm information.

[0187] The word segmentation unit is used to perform word segmentation processing on the keywords in the deleted alarm information using separators.

[0188] Optionally, in a data alarm analysis device provided in another embodiment of the present application, the similarity calculation unit 803 includes:

[0189] The detection unit is used to detect whether there is an intersection between the alarm information and the non-important keywords corresponding to each piece of knowledge in the knowledge base.

[0190] The intersection obtaining unit is configured to obtain the intersection of the alarm information and the non-important keywords corresponding to the knowledge if there is an intersection between the alarm information and the non-important keywords corresponding to the knowledge.

[0191] The merging unit is used to merge the warning information with the non-important keywords corresponding to the knowledge to obtain a union.

[0192] The calculation unit is used to divide the intersection by the union to obtain the similarity between the warning information and the non-important keywords corresponding to the knowledge.

[0193] The first determining unit is configured to determine that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero if there is no intersection between the warning information and the non-important keywords corresponding to the knowledge.

[0194] Optionally, in a data alarm analysis device provided in another embodiment of the present application, the knowledge determination unit 804 includes:

[0195] The similarity search unit is used to search for each target similarity greater than a preset threshold from the similarities between the warning information and the non-important keywords corresponding to each piece of knowledge.

[0196] The second determining unit is used to determine the knowledge corresponding to each target similarity as target knowledge, and obtain multiple target knowledge.

[0197] Optionally, another embodiment of the present application provides a data alarm analysis device, further comprising:

[0198] The acquisition unit is used to obtain target warning information from the knowledge base.

[0199] The target alarm information refers to the information newly registered or modified by the database administrator.

[0200] The processing unit is used to perform word segmentation processing on the target warning information.

[0201] The second extraction unit is configured to extract keywords of the target warning information from the segmented target warning information.

[0202] The first updating unit is configured to update the target keyword into the important keyword queue if there is a target keyword with a number among the keywords.

[0203] The second updating unit is configured to update each keyword to a non-important keyword queue if each keyword does not carry a number.

[0204] Optionally, in a data alarm analysis device provided in another embodiment of the present application, the link output unit 806 includes:

[0205] The first sorting unit is configured to sort each piece of target knowledge in descending order according to the similarity of the non-important keywords corresponding to each piece of target knowledge if the similarity of each piece of target knowledge to the non-important keywords is greater than a preset threshold.

[0206] The second sorting unit is configured to sort each piece of target knowledge in descending order according to the system name and value score of each piece of target knowledge if each piece of target knowledge refers to the same knowledge as the important keyword.

[0207] The output unit is used to output the access links corresponding to the various pieces of target knowledge in descending order.

[0208] Optionally, another embodiment of the present application provides a data alarm analysis device, further comprising:

[0209] The score acquisition unit is used to obtain the problem management scores corresponding to multiple database administrators according to the monthly, quarterly or annual reading time.

[0210] The problem management score refers to the score that the database administrator scores for solving the problems registered by himself.

[0211] The statistical unit is used to collect statistics on the problem management scores corresponding to each database administrator to obtain the total problem management scores corresponding to each database administrator.

[0212] The ranking unit is used to rank each database administrator according to the total problem management score corresponding to each database administrator.

[0213] Optionally, another embodiment of the present application provides a data alarm analysis device, further comprising:

[0214] The search unit is used to search for the corresponding database administrator according to the host name of the database when an alarm message is detected in the database.

[0215] The feedback unit is used to feed back prompt information to the database administrator.

[0216] The prompt information is used to prompt the database administrator to analyze and register the alarm information existing in the database.

[0217] It should be noted that the specific working process of each unit provided in the above embodiments of the present application can refer to the corresponding steps in the above method embodiments, and will not be repeated here.

[0218] In summary, the embodiment of the present application provides a data alarm analysis device, which regularly obtains alarm information from the database, and then automatically searches for the knowledge with the highest matching degree with the alarm information from the knowledge base, and provides it to the database administrator to solve the alarm information, thereby effectively solving the problem of low efficiency of manual knowledge search.

[0219] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0220] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data alarm analysis method, characterized in that: include: Obtain alarm information from the database at preset intervals; Determine whether there are important keywords in the alarm information; wherein the important keywords refer to keywords carrying numbers; If the important keyword does not exist in the alarm information, extracting the English keyword corresponding to the alarm information from the alarm information; Deleting the English keyword from the warning information; Using separators to perform word segmentation on the deleted keywords in the warning information; Calculating the similarity between the warning information and the non-important keywords corresponding to each piece of knowledge in the knowledge base respectively; Determining each target knowledge from each piece of knowledge according to each similarity; wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold; If the important keyword exists in the warning information, searching for each target knowledge item that is the same as the important keyword from the knowledge base; Output the access link corresponding to each piece of target knowledge; The step of respectively calculating the similarity between the warning information and the non-important keywords corresponding to each piece of knowledge in the knowledge base includes: For each piece of knowledge in the knowledge base, detecting whether there is an intersection between the warning information and the non-important keywords corresponding to the knowledge; If there is an intersection between the warning information and the non-important keywords corresponding to the knowledge, obtaining the intersection between the warning information and the non-important keywords corresponding to the knowledge; Merging the warning information with non-important keywords corresponding to the knowledge to obtain a union; Dividing the intersection by the union to obtain the similarity between the warning information and the non-important keywords corresponding to the knowledge; If there is no intersection between the warning information and the non-important keywords corresponding to the knowledge, it is determined that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero.

2. The method according to claim 1, characterized in that Determining each target knowledge item from each piece of knowledge item based on each similarity includes: Searching for each target similarity greater than a preset threshold from the similarities between the warning information and the non-important keywords corresponding to each piece of knowledge; The knowledge corresponding to each target similarity is respectively determined as target knowledge to obtain multiple pieces of target knowledge.

3. The method according to claim 1, characterized in that Also includes: Obtaining target alarm information from a knowledge base; wherein the target alarm information refers to information newly registered or modified by a database administrator; Performing word segmentation processing on the target warning information; Extracting each keyword of the target warning information from the target warning information after word segmentation; If there is a target keyword with a number among the keywords, the target keyword is updated to the important keyword queue; If each of the keywords does not carry a number, each of the keywords is updated to a non-important keyword queue.

4. The method according to claim 1, wherein Outputting the access link corresponding to the target knowledge includes: If the similarity between each target knowledge item and the non-important keyword is greater than a preset threshold, sort the target knowledge items in descending order according to the similarity between each target knowledge item and the non-important keyword; If each of the target knowledge items refers to the same knowledge as the important keyword, then sort the target knowledge items in descending order according to their system names and value scores; According to the descending order of each target knowledge, the access link corresponding to each target knowledge is output in sequence.

5. The method according to claim 1, characterized in that Also includes: Obtaining problem management scores corresponding to multiple database administrators based on monthly, quarterly, or annual reading times; wherein the problem management scores refer to scores of the database administrators for solving the problems registered by themselves; Counting the problem management scores corresponding to the database administrators to obtain the total problem management scores corresponding to the database administrators; The database administrators are ranked according to their corresponding total problem management scores.

6. The method according to claim 1, characterized in that Also includes: When an alarm message is detected in the database, the corresponding database administrator is searched according to the host name of the database; Feedback prompt information to the database administrator; wherein the prompt information is used to prompt the database administrator to analyze and register the alarm information existing in the database.

7. A data alarm analysis device, characterized in that: include: An information acquisition unit, configured to acquire alarm information from a database at preset intervals; A judging unit, configured to judge whether an important keyword exists in the warning information; wherein the important keyword refers to a keyword carrying a number; a first extraction unit, configured to extract English keywords corresponding to the alarm information from the alarm information if the important keywords do not exist in the alarm information; a deleting unit, configured to delete the English keyword from the warning information; A word segmentation unit, configured to perform word segmentation processing on the keywords in the deleted warning information using separators; A similarity calculation unit, for respectively calculating the similarity between the warning information and the non-important keywords corresponding to each piece of knowledge in the knowledge base; A knowledge determination unit, configured to determine each target knowledge from each piece of knowledge according to each similarity; wherein the similarity of the non-important keywords corresponding to the target knowledge is greater than a preset threshold; a knowledge search unit, configured to search for target knowledge items identical to the important keywords from a knowledge base if the important keywords exist in the warning information; A link output unit, configured to output access links corresponding to each piece of target knowledge; Wherein, the similarity calculation unit includes: a detection unit, configured to detect, for each piece of knowledge in the knowledge base, whether there is an intersection between the warning information and the non-important keywords corresponding to the knowledge; an intersection obtaining unit, configured to obtain the intersection of the warning information and the non-important keywords corresponding to the knowledge if there is an intersection between the warning information and the non-important keywords corresponding to the knowledge; a merging unit, configured to merge the warning information with non-important keywords corresponding to the knowledge to obtain a union; a calculation unit, configured to divide the intersection by the union to obtain a similarity between the warning information and the non-important keywords corresponding to the knowledge; The first determining unit is configured to determine that the similarity between the warning information and the non-important keywords corresponding to the knowledge is zero if there is no intersection between the warning information and the non-important keywords corresponding to the knowledge.

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