Database early warning method, device, electronic device and computer-readable storage medium

Abstract

The present invention provides a database early warning method, device, electronic device and computer-readable storage medium. The database early warning method includes: obtaining early warning data for a target database, the early warning data including early warning content and early warning type; determining the early warning level of the early warning data according to the preset conditions and early warning content corresponding to the early warning type, the early warning level representing the degree of influence of the early warning data on the target database; correcting the early warning level using a predetermined analysis algorithm according to the historical early warning data and early warning level belonging to the early warning type to obtain a corrected early warning level; generating target early warning information, target personnel information and target early warning notification method based on the corrected early warning level and early warning data and a preset early warning model, the target early warning information including early warning content and the corrected early warning level; and performing a database early warning according to the target early warning information, target personnel information and target early warning notification method.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and more specifically to a database early warning method, device, electronic device and computer-readable storage medium. Background Art

[0002] Databases are collections used to store, manage, and organize data. Databases play a vital role in modern information systems. Database monitoring and early warning can help administrators obtain database operation status and performance data in a timely manner, and discover and solve potential problems as early as possible. When conducting database evaluation and early warning, early warning data is a key factor, and the analysis and evaluation of early warning data is of great significance. Related technologies for database monitoring and early warning generally push early warning information directly to relevant personnel after receiving the early warning information, which is prone to sending meaningless early warning information to relevant personnel. Summary of the invention

[0003] In view of the above problems, the present invention provides a database early warning method, device, electronic device and computer-readable storage medium.

[0004] According to a first aspect of the present invention, there is provided a database early warning method, comprising:

[0005] Acquire warning data for a target database, wherein the warning data includes warning content and warning type;

[0006] Determine the warning level of the warning data according to the preset conditions and warning content corresponding to the warning type, wherein the warning level represents the degree of impact of the warning data on the target database;

[0007] According to the historical warning data and the warning level belonging to the warning type, the warning level is corrected by using a predetermined analysis algorithm to obtain a corrected warning level;

[0008] According to the revised warning level and warning data, based on the preset warning model, target warning information, target personnel information and target warning notification method are generated, wherein the target warning information includes warning content and the revised warning level; and

[0009] Database warning is carried out based on target warning information, target personnel information and target warning notification method.

[0010] According to an embodiment of the present invention, based on the historical warning data and the warning level belonging to the warning type, the warning level is corrected using a predetermined analysis algorithm, and the corrected warning level includes:

[0011] Scoring the warning data using a predetermined analysis algorithm according to the frequency of occurrence of warnings of the warning type in the historical warning data belonging to the warning type and the degree of influence of the warning type on the target database, thereby obtaining a first scoring result;

[0012] Obtaining a second scoring result according to the first scoring result and the scoring result corresponding to the warning level;

[0013] According to the second scoring result and the preset scoring-grading conditions, the grade corresponding to the second scoring result is determined to obtain a revised warning grade.

[0014] According to an embodiment of the present invention, obtaining a second scoring result according to the first scoring result and the warning level includes:

[0015] Obtaining a third scoring result according to the preset first weight and the first scoring result;

[0016] According to the preset scoring-grading conditions and warning levels, the scoring results corresponding to the warning levels are obtained;

[0017] Scoring according to the preset second weight and the scoring result corresponding to the warning level to obtain a fourth scoring result;

[0018] The third scoring result and the fourth scoring result are summed to obtain a second scoring result.

[0019] According to an embodiment of the present invention, the predetermined analysis algorithm includes a plurality of indicator layers, each indicator layer having a corresponding weight vector, the weight vector characterizing the frequency of occurrence of the warning type and the degree of impact on the target database;

[0020] According to the frequency of occurrence of warnings of the warning type in the historical warning data belonging to the warning type and the degree of impact on the target database, the warning data is analyzed using a predetermined analysis algorithm to obtain the first scoring result including:

[0021] Determine a target indicator layer corresponding to the warning type from multiple indicator layers according to the warning type;

[0022] The fifth scoring result is obtained according to the weight vector of the warning content and the target indicator layer;

[0023] The fifth scoring result is normalized to obtain the first scoring result.

[0024] According to an embodiment of the present invention, the preset warning model includes a classification tree model and a regression tree model;

[0025] According to the revised warning level, warning data and preset warning model, the target warning information, target personnel information and target warning notification methods are generated, including:

[0026] According to the warning type, a target preset warning model corresponding to the warning type is determined from the classification tree model and the regression tree model;

[0027] According to the target preset warning model, warning content and revised warning level, target warning information, target personnel information and target warning notification method are generated.

[0028] According to an embodiment of the present invention, the target warning information also includes a method for processing historical warning data belonging to the warning type;

[0029] The target early warning notification method includes the sending channel and sending frequency.

[0030] A second aspect of the present invention provides a database early warning device, which includes:

[0031] An acquisition module is used to acquire early warning data for a target database, wherein the early warning data includes early warning content and early warning type;

[0032] A determination module, used to determine the warning level of the warning data according to the preset conditions and warning content corresponding to the warning type, wherein the warning level represents the degree of influence of the warning data on the target database;

[0033] A correction module, used to correct the warning level according to the historical warning data and the warning level belonging to the warning type by using a predetermined analysis algorithm to obtain a corrected warning level;

[0034] A generation module, used to generate target warning information, target personnel information and target warning notification method according to the revised warning level and warning data based on a preset warning model, wherein the target warning information includes warning content and the revised warning level; and

[0035] The early warning module is used to issue database early warnings based on target early warning information, target personnel information and target early warning notification methods.

[0036] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0037] The fourth aspect of the present invention further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0038] The fifth aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0039] According to the embodiment of the present invention, by dividing the database warning data into warning levels, and then combining the predetermined analysis algorithm to correct the warning level according to the historical data of the type to which the warning data belongs, the corrected warning level is closer to the actual operation of the database, which helps to filter out meaningless warning information; in addition, through the preset warning model, intelligent matching is performed to generate personalized warning push information, so that managers can grasp the operation status of the database, instead of directly pushing all warning information to all personnel, which reduces the difficulty of judging the warning information. That is, the readability of the warning information is guaranteed, and it is also convenient for relevant personnel to track and handle events after the problem occurs, while ensuring the stable operation of the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0041] Figure 1 A diagram schematically shows an application scenario in which the database early warning method according to an embodiment of the present invention can be applied;

[0042] Figure 2 A flowchart of a database early warning method according to an embodiment of the present invention is schematically shown;

[0043] Figure 3 A schematic diagram of warning level classification according to an embodiment of the present invention is shown schematically;

[0044] Figure 4 A schematic diagram of a target warning information pusher according to an embodiment of the present invention is shown;

[0045] Figure 5 A flowchart of a database early warning method according to an embodiment of the present invention is schematically shown;

[0046] Figure 6 A schematic diagram of a structure of a database early warning device according to an embodiment of the present invention is shown;

[0047] Figure 7 A block diagram of an electronic device suitable for implementing a database early warning method according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0048] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0049] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0050] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0051] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0052] In the embodiments of the present invention, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage and other aspects of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0053] In the embodiment of the present invention, before obtaining or collecting the user's personal information, the user's authorization or consent is obtained.

[0054] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0055] In the conception of realizing the present invention, the previous database monitoring and early warning means mainly collect various indicators of the database through monitoring software and set thresholds; when a certain value reaches the threshold, the management personnel are notified through SMS, WeChat, email, etc., lacking analysis and evaluation of the early warning information. The early warning information received by the management personnel is poorly readable, and it is even difficult to grasp the real situation, and many problems will arise, such as all personnel receive a large number of early warning text messages but some personnel do not pay attention to these information; it is difficult to identify specific problems based on the early warning information; the early warning information is repeated and does not require any processing; after the early warning information is generated, no personnel responds, etc.

[0056] Although most databases have implemented monitoring and early warning, they have not analyzed the monitoring and early warning information. In most cases, the early warning information is directly pushed to relevant personnel.

[0057] Therefore, it is necessary to use reasonable evaluation and early warning methods to timely discover potential risks or problems, push effective early warning information and detailed instructions to designated personnel, and take appropriate measures to prevent and respond.

[0058] Based on this, an embodiment of the present invention provides a database early warning method, including:

[0059] Acquire warning data for a target database, wherein the warning data includes warning content and warning type;

[0060] Determine the warning level of the warning data according to the preset conditions and warning content corresponding to the warning type, wherein the warning level represents the degree of impact of the warning data on the target database;

[0061] According to the historical warning data and the warning level belonging to the warning type, the warning level is corrected by using a predetermined analysis algorithm to obtain a corrected warning level;

[0062] According to the revised warning level and warning data, based on the preset warning model, target warning information, target personnel information and target warning notification method are generated, wherein the target warning information includes warning content and the revised warning level; and

[0063] Database warning is carried out based on target warning information, target personnel information and target warning notification method.

[0064] Figure 1 The following schematically shows an exemplary application scenario 100 to which the database early warning method according to an embodiment of the present invention can be applied. It should be noted that: Figure 1 The examples shown are merely examples of application scenarios to which the embodiments of the present invention can be applied, to help those skilled in the art understand the technical content of the present invention, but do not mean that the embodiments of the present invention cannot be used in other devices, systems, environments or scenarios.

[0065] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0066] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only for example).

[0067] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0068] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0069] It should be noted that the database early warning method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the database early warning system provided in the embodiment of the present invention can generally be set in the server 105. The database early warning method provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the database early warning system provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0070] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0071] The following will be based on Figure 1 The scene described by Figure 2~Figure 5The database early warning method of the disclosed embodiment is described in detail.

[0072] Figure 2 A flowchart of a database early warning method according to an embodiment of the present invention is schematically shown; Figure 3 A schematic diagram of warning level classification according to an embodiment of the present invention is schematically shown.

[0073] like Figure 2 As shown, the method includes operations S201 to S205.

[0074] Operation S201: Acquire warning data for a target database, wherein the warning data includes warning content and warning type.

[0075] According to an embodiment of the present invention, early warning data refers to data generated during the operation of the database for monitoring and identifying abnormal situations, predicting potential problems and opportunities. Early warning content refers to specific warning or notification information in the early warning data, including parameters of specific warning values, such as CPU usage exceeding 90%, which can be understood as 90% being a parameter within the early warning content, or disk space usage exceeding 80%, which can be understood as 80% being a parameter within the early warning content, and can also include a specific description of the early warning data such as the location and time of occurrence. Early warning type refers to the type of problem warned by the early warning data, such as hardware, operating system, network, SQL statement, lock, etc.

[0076] For example, Figure 3 As shown, the database generates warning data when a warning occurs by monitoring the operating status, including basic monitoring warning information, network monitoring warning information, etc.

[0077] For example, a warning data may include:

[0078] Warning type: Hardware; Warning content: Server hardware failure warning, the hard disk is expected to have 10% of its life remaining. Warning time: xx / xx / xx / xx:xx / xx.

[0079] Operation S202: determining a warning level of the warning data according to a preset condition and a warning content corresponding to the warning type, wherein the warning level represents the degree of influence of the warning data on the target database.

[0080] According to an embodiment of the present invention, the warning level refers to a level divided according to the degree of possible harm, urgency and development trend, for example Figure 3As shown, the warning data is divided into important warning, major warning, minor warning, and general warning. The preset conditions corresponding to the warning type refer to the conditions for classifying the warning data. The preset conditions of the warning level can be set by presetting the threshold. When the parameters in the warning content exceed the threshold of the preset parameters, for example, the parameters exceeding 10 are important warnings, and between 5 and 10 are major warnings.

[0081] Operation S203, based on the historical warning data and the warning level belonging to the warning type, the warning level is corrected using a predetermined analysis algorithm to obtain a corrected warning level.

[0082] According to an embodiment of the present invention, historical warning data of a warning type refers to data records about a specific warning type, which includes all relevant information about warning events that have occurred in the past, such as the time, duration, scope of impact, severity, etc. of the warning. These data are used to analyze and predict warning events that may occur in the future. A predetermined analysis algorithm refers to a predetermined or trained algorithm for processing and analyzing data, which can learn from historical data and correct the warning level. The corrected warning level refers to the warning level obtained after processing by the predetermined analysis algorithm. This level takes into account historical data and current conditions, is more accurate than the original warning level, and can better reflect the severity of the actual situation.

[0083] Operation S204, generating target warning information, target personnel information and target warning notification method according to the revised warning level and warning data based on a preset warning model, wherein the target warning information includes warning content and the revised warning level.

[0084] According to an embodiment of the present invention, the preset warning model refers to a predefined model for generating specific warning information based on warning data and warning levels. The model may include a series of rules and logic for determining when and how to generate warning information, and may also include personnel information, such as personnel levels and personnel preferences for warning types. Target warning information refers to the final warning information generated according to the preset warning model, which includes the warning content and the revised warning level, and may specifically be the text or picture content of the information to be pushed when the warning is issued. Target personnel information refers to the detailed information of the personnel who need to receive the warning information, including their identity, level, preferences, contact information, etc., to ensure that the warning information can be accurately sent to the correct recipient. The target warning notification method refers to the method of sending the warning information.

[0085] In some specific embodiments of the present invention, the target warning information also includes a method for processing historical warning data belonging to the warning type.

[0086] According to an embodiment of the present invention, a method for processing historical warning data belonging to a warning type refers to a series of technologies and methods for processing historical warning data of a specific warning type when a warning has occurred in the past, and may also include processing results.

[0087] In some specific embodiments of the present invention, the target warning notification method includes a sending channel and a sending frequency.

[0088] According to an embodiment of the present invention, the sending channel may include email, SMS, application push notification, phone or other communication methods. Furthermore, it can be known through the preset warning model that the notification method selected depends on the information of the target person and the urgency of the warning. The sending frequency refers to the frequency of pushing the target warning information, such as once a day, once an hour, etc.

[0089] Figure 4 A schematic diagram of personnel pushing target warning information according to an embodiment of the present invention is schematically shown.

[0090] according to Figure 4 It can be seen that after being processed by the preset warning model, the target warning information can be pushed according to the warning type, such as warning for Class A system, warning for Class B system, etc., and different levels of target warning information can be selected according to the rank of the personnel.

[0091] Furthermore, after being processed by the preset warning model, it can also be pushed according to the preferences of the target warning personnel:

[0092] For example, network management office personnel only receive important and major warning information about the network and network systems, and only need to be reminded once by SMS.

[0093] For example, the leader of the database group only receives all warning information about all databases and needs to be reminded by phone once.

[0094] Operation S205: Perform a database warning according to the target warning information, target personnel information and target warning notification method.

[0095] According to an embodiment of the present invention, performing database warning refers to pushing target warning information according to target personnel information and target warning notification mode, thereby achieving warning of the target database.

[0096] According to the embodiment of the present invention, by dividing the database warning data into warning levels, and then combining the predetermined analysis algorithm to correct the warning level according to the historical data of the type to which the warning data belongs, the corrected warning level is closer to the actual operation of the database, which helps to filter out meaningless warning information; in addition, through the preset warning model, intelligent matching is performed to generate personalized warning push information, so that managers can grasp the operation status of the database, instead of directly pushing all warning information to all personnel, which reduces the difficulty of judging the warning information. That is, the readability of the warning information is guaranteed, and it is also convenient for relevant personnel to track and handle events after the problem occurs, while ensuring the stable operation of the database.

[0097] Figure 5 The flowchart of the database early warning method according to an embodiment of the present invention is schematically shown.

[0098] According to an embodiment of the present invention, in operation S203, Figure 5 As shown, it also includes operations S501 to S503.

[0099] Operation S501 : Scoring the warning data using a predetermined analysis algorithm according to the frequency of occurrence of warnings of the warning type in the historical warning data belonging to the warning type and the degree of influence of the warning type on the target database, to obtain a first scoring result.

[0100] Operation S502: Obtain a second scoring result according to the first scoring result and the scoring result corresponding to the warning level.

[0101] Operation S503: determining the level corresponding to the second scoring result according to the second scoring result and the preset scoring-grading condition, and obtaining a revised warning level.

[0102] According to an embodiment of the present invention, in operation S501, the first scoring result refers to the result obtained after scoring the warning data using a predetermined analysis algorithm, which reflects the importance and urgency of the warning data and is an important reference for the correction of the warning level. The historical warning data of each warning type includes information on the frequency of occurrence of this warning type and the degree of impact on the target database. The predetermined analysis algorithm can be combined with these historical information to analyze and process the warning data belonging to this specific warning type to obtain the first scoring result.

[0103] For example, a warning data is classified as an unimportant warning according to its corresponding preset conditions. However, by analyzing this type of historical data through a predetermined analysis algorithm, it is found that although the numerical change is very small, it will have a significant impact on the operation of the database. In this case, the predetermined analysis algorithm will obtain a first scoring result based on the historical data and the specific warning content of this warning data.

[0104] According to an embodiment of the present invention, in operation S502, the scoring result corresponding to the warning level refers to the warning level corresponding to a specific score through a preset scoring-grading condition. For example, an important warning corresponds to 10 points, and a minor warning corresponds to 5 points.

[0105] According to an embodiment of the present invention, in operation S503, the preset scoring-grading condition refers to a preset condition for converting the warning level into a score or converting a specific score into a warning level. The level of the second scoring result is converted according to the preset scoring-grading condition. For example, if the second scoring result is 10 points, corresponding to an important warning, then the revised warning level is an important warning.

[0106] According to an embodiment of the present invention, by combining the frequency and impact of historical data and the specific content of real-time warning data, the system can more accurately evaluate the importance and urgency of the warning, and can dynamically adjust the warning level based on real-time analysis and historical trends, so that the warning is more in line with the current security situation. Automated scoring and level correction reduce the deviation of human judgment and improve the reliability of the warning system, thereby reducing false alarms and missed alarms.

[0107] In some specific embodiments of the present invention, in operation S501, the predetermined analysis algorithm includes a plurality of indicator layers, each indicator layer has a corresponding weight vector, and the weight vector represents the frequency of occurrence of the warning type and the degree of influence on the target database;

[0108] According to the frequency of occurrence of warnings of the warning type in the historical warning data belonging to the warning type and the degree of impact on the target database, the warning data is analyzed using a predetermined analysis algorithm to obtain the first scoring result including:

[0109] Determine a target indicator layer corresponding to the warning type from multiple indicator layers according to the warning type;

[0110] The fifth scoring result is obtained according to the weight vector of the warning content and the target indicator layer;

[0111] The fifth scoring result is normalized to obtain the first scoring result.

[0112] According to an embodiment of the present invention, an indicator layer refers to different evaluation dimensions or categories defined in a predetermined analysis algorithm, and each indicator layer targets specific warning characteristics or attributes, such as frequency of occurrence, degree of impact, etc. Each indicator layer has a weight vector, which is obtained through pairwise comparison and characterizes the frequency of occurrence of the warning type and the degree of impact on the target database. The weight vector is a set of weights associated with each indicator layer, which characterizes the relative importance of the frequency of occurrence of the warning type and the degree of impact on the target database. The weight vector is used to give different indicators different degrees of importance during the scoring process. The target indicator layer refers to the indicator layer corresponding to a specific warning type. When analyzing specific warning data, the system will determine the indicator layer that is most relevant to the warning type from multiple indicator layers.

[0113] Furthermore, the predetermined analysis algorithm also includes a target layer, the purpose of which is to analyze the frequency of warning types and the degree of impact on the target database, so as to ensure the security and stable operation of the database.

[0114] According to an embodiment of the present invention, the fifth scoring result may be calculated by the following formula:

[0115] Fifth Rating Result = (Warning content value × weight vector value).

[0116] According to an embodiment of the present invention, the normalization process is performed to ensure that the scoring results of different indicator layers can be compared and accumulated on the same scale to ensure the comparability of the scoring results. The normalization process can be performed by the following formula:

[0117] The first scoring result = (the fifth scoring result − mean) / standard deviation.

[0118] In some specific embodiments of the present invention, for very important business systems, the warning level and frequency can be increased, and the warning information can be pushed to designated personnel in a timely manner.

[0119] In some specific embodiments of the present invention, statistical analysis can be performed on the warning information to integrate the same or similar information to increase the readability of the information so that management personnel can make effective judgments.

[0120] In some specific embodiments of the present invention, human intervention, setting, supplementation and adjustment can be performed to achieve an early warning effect for a special situation.

[0121] According to an embodiment of the present invention, the analytic hierarchy process can be used to analyze the warning data based on the historical warning frequency of the warning type and the dimension of the degree of impact on the target database. It can systematically evaluate the severity of the warning event based on the warning content and the preset weight vector, and give a quantitative scoring result, thereby providing decision support for database managers.

[0122] In some specific embodiments of the present invention, in operation S502, the method for obtaining the second scoring result includes:

[0123] Obtaining a third scoring result according to the preset first weight and the first scoring result;

[0124] According to the preset scoring-grading conditions and warning levels, the scoring results corresponding to the warning levels are obtained;

[0125] Scoring according to the preset second weight and the scoring result corresponding to the warning level to obtain a fourth scoring result;

[0126] The third scoring result and the fourth scoring result are summed to obtain a second scoring result.

[0127] According to an embodiment of the present invention, the preset scoring-grading condition refers to a preset condition for converting a warning level into a score or converting a specific score into a warning level. The warning level score refers to the score result after converting the warning level according to the preset scoring-grading condition.

[0128] According to an embodiment of the present invention, the first weight and the second weight are preset by the database administrator according to actual needs. The first weight can represent the importance of the result obtained by the predetermined analysis algorithm to the revised warning level, and the second weight can represent the importance of the original divided warning level to the revised warning level. For example, if the administrator believes that the original divided warning level has low accuracy and is not very referenceable, the second weight can be set lower and the first weight can be set higher, for example, the first weight is set to 0.7 and the second weight is set to 0.3.

[0129] Furthermore, after assigning a first weight to the first scoring result, a new third scoring result will be obtained. After assigning a second weight to the warning level score, a new fourth scoring result will be obtained. After summing the third scoring result and the fourth scoring result, the second scoring result can be obtained, which is then used to correct the warning level.

[0130] According to an embodiment of the present invention, by combining the first scoring result and the warning level score and using weight adjustment, the actual situation can be more accurately reflected, thereby improving the accuracy of the warning. The scoring results can also be dynamically adjusted through experience based on the actual operating status of the database and actual warning needs. The system can quickly respond to changing environments and conditions, comprehensively consider historical data and current analysis results, and reduce the possibility of false alarms and missed alarms.

[0131] According to an embodiment of the present invention, the preset warning model includes a classification tree model and a regression tree model;

[0132] According to the revised warning level, warning data and preset warning model, the target warning information, target personnel information and target warning notification methods are generated, including:

[0133] According to the warning type, a target preset warning model corresponding to the warning type is determined from the classification tree model and the regression tree model;

[0134] According to the target preset warning model, warning content and revised warning level, target warning information, target personnel information and target warning notification method are generated.

[0135] According to an embodiment of the present invention, the preset warning model can use a decision tree model. When the warning result is discrete, such as a warning of whether a certain type of fault occurs, a classification tree model is used; when the warning result is continuous, such as predicting the time of occurrence or the extent of impact of a fault, a regression tree model is used.

[0136] According to an embodiment of the present invention, if the warning type is suitable for using a classification tree model, then the warning content is input into the model, and the model will classify the warning results according to preset rules and learned patterns. If the warning type is suitable for using a regression tree model, then the warning content is input into the model, and the model will predict the continuous value of the warning result, such as the specific time when the warning occurs or the degree of impact.

[0137] Furthermore, based on the output of the model and combined with the revised warning level, specific warning information is generated. This information includes a detailed description of the warning, possible impacts, recommended countermeasures, etc. According to the severity and type of the warning, determine the target personnel who need to be notified. For example, a serious warning may require notification of senior management, while a general warning may only require notification of technical personnel. Determine the notification method, such as email, text message, APP push, etc., to ensure that the warning information can be conveyed to the relevant personnel in a timely and accurate manner.

[0138] According to the embodiment of the present invention, by combining the classification tree model and the regression tree model, the early warning system can more accurately predict and classify early warning events, improve the response speed and processing efficiency of early warnings. In addition, it can flexibly select the most appropriate model according to the specific circumstances of the early warning, and improve the adaptability and accuracy of the early warning system.

[0139] Based on the above database early warning method, the present invention also provides a database early warning device. Figure 6 The device is described in detail.

[0140] Figure 6 The structural block diagram of the database early warning device according to an embodiment of the present invention is schematically shown.

[0141] like Figure 6 As shown, the database warning device 600 of this embodiment includes an acquisition module 610 , a determination module 620 , a correction module 630 , a generation module 640 and a warning module 650 .

[0142] The acquisition module 610 is used to acquire the warning data for the target database, wherein the warning data includes the warning content and the warning type. In one embodiment, the acquisition module 610 can be used to perform the operation S201 described above, which will not be described in detail here.

[0143] The determination module 620 is used to determine the warning level of the warning data according to the preset conditions and warning content corresponding to the warning type, wherein the warning level represents the impact degree of the warning data on the target database. In one embodiment, the determination module 620 can be used to perform the operation S202 described above, which will not be repeated here.

[0144] The correction module 630 is used to correct the warning level according to the historical warning data and the warning level of the warning type using a predetermined analysis algorithm to obtain a corrected warning level. In one embodiment, the correction module 630 can be used to perform the operation S203 described above, which will not be repeated here.

[0145] The generation module 640 is used to generate target warning information, target personnel information and target warning notification method according to the revised warning level and warning data based on the preset warning model, wherein the target warning information includes warning content and the revised warning level. In one embodiment, the generation module 640 can be used to perform the operation S204 described above, which will not be repeated here.

[0146] The early warning module 650 is used to perform a database early warning according to the target early warning information, the target personnel information and the target early warning notification method. In one embodiment, the early warning module 650 can be used to perform the operation S205 described above, which will not be described in detail here.

[0147] According to the embodiment of the present invention, by dividing the database warning data into warning levels, and then combining the predetermined analysis algorithm to correct the warning level according to the historical data of the type to which the warning data belongs, the corrected warning level is closer to the actual operation of the database, which helps to filter out meaningless warning information; in addition, through the preset warning model, intelligent matching is performed to generate personalized warning push information, so that managers can grasp the operation status of the database, instead of directly pushing all warning information to all personnel, which reduces the difficulty of judging the warning information. That is, the readability of the warning information is guaranteed, and it is also convenient for relevant personnel to track and handle events after the problem occurs, while ensuring the stable operation of the database.

[0148] According to an embodiment of the present invention, any multiple modules among the acquisition module 610, the determination module 620, the correction module 630, the generation module 640 and the early warning module 650 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the acquisition module 610, the determination module 620, the correction module 630, the generation module 640 and the early warning module 650 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the acquisition module 610, the determination module 620, the correction module 630, the generation module 640 and the warning module 650 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0149] Figure 7 A block diagram of an electronic device suitable for implementing a database early warning method according to an embodiment of the present invention is schematically shown.

[0150] Figure 7 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present invention is schematically shown. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0151] like Figure 7As shown, the electronic device 700 according to an embodiment of the present invention includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0152] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in one or more memories.

[0153] According to an embodiment of the present invention, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0154] According to an embodiment of the present invention, the method flow according to an embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above-mentioned functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0155] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0156] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0157] For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703 .

[0158] An embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the database early warning method provided by the embodiment of the present invention.

[0159] When the computer program is executed by the processor 701, the above functions defined in the system / device of the embodiment of the present invention are executed. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0160] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0161] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways, even if such a combination or combination is not explicitly recorded in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways. All these combinations and / or combinations fall within the scope of the present invention.

[0163] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A database early warning method, comprising: Acquire warning data for a target database, wherein the warning data includes warning content and warning type; Determining the warning level of the warning data according to the preset conditions corresponding to the warning type and the warning content, wherein the warning level represents the degree of influence of the warning data on the target database; According to the historical warning data belonging to the warning type and the warning level, the warning level is corrected by using a predetermined analysis algorithm to obtain a corrected warning level; According to the revised warning level and the warning data, based on a preset warning model, generating target warning information, target personnel information and target warning notification method, wherein the target warning information includes the warning content and the revised warning level; and A database warning is performed according to the target warning information, the target personnel information and the target warning notification method.

2. The method according to claim 1, wherein: The method of modifying the warning level by using a predetermined analysis algorithm based on the historical warning data belonging to the warning type and the warning level to obtain the modified warning level includes: According to the frequency of occurrence of warnings of the warning type in the historical warning data belonging to the warning type and the degree of influence of the warning type on the target database, the warning data is scored using a predetermined analysis algorithm to obtain a first scoring result; Obtaining a second scoring result according to the first scoring result and the scoring result corresponding to the warning level; According to the second scoring result and the preset scoring-grading condition, the grade corresponding to the second scoring result is determined to obtain the revised warning grade.

3. The method according to claim 2, wherein: The obtaining of the second scoring result according to the first scoring result and the scoring result corresponding to the warning level includes: Obtaining a third scoring result according to the preset first weight and the first scoring result; Obtaining a scoring result corresponding to the warning level according to the preset scoring-grading condition and the warning level; Obtaining a fourth scoring result according to the preset second weight and the scoring result corresponding to the warning level; The third scoring result and the fourth scoring result are summed to obtain the second scoring result.

4. The method according to claim 2, wherein: The predetermined analysis algorithm includes a plurality of indicator layers, each of which has a corresponding weight vector, and the weight vector represents the occurrence frequency of the warning type and the degree of influence on the target database; The first scoring result is obtained by analyzing the warning data using a predetermined analysis algorithm according to the frequency of occurrence of the warning of the warning type in the historical warning data belonging to the warning type and the degree of influence on the target database: Determining a target indicator layer corresponding to the warning type from multiple indicator layers according to the warning type; Obtaining a fifth scoring result according to the warning content and the weight vector of the target indicator layer; The first scoring result is obtained by normalizing the fifth scoring result.

5. The method according to claim 1, wherein: The preset early warning model includes a classification tree model and a regression tree model; The generating of target warning information, target personnel information and target warning notification method according to the revised warning level, the warning data and the preset warning model includes: Determine a target preset warning model corresponding to the warning type from a classification tree model and a regression tree model according to the warning type; The target warning information, the target personnel information and the target warning notification method are generated according to the target preset warning model, the warning content and the revised warning level.

6. The method according to claim 1, wherein: The target warning information also includes a method for processing historical warning data belonging to the warning type; The target early warning notification method includes a sending channel and a sending frequency.

7. A database early warning device, comprising: An acquisition module, used to acquire warning data for a target database, wherein the warning data includes warning content and warning type; A determination module, configured to determine the warning level of the warning data according to the preset conditions corresponding to the warning type and the warning content, wherein the warning level represents the degree of influence of the warning data on the target database; A correction module, used to correct the warning level using a predetermined analysis algorithm according to the historical warning data belonging to the warning type and the warning level, to obtain a corrected warning level; a generating module, configured to generate target warning information, target personnel information and target warning notification method according to the revised warning level and the warning data and based on a preset warning model, wherein the target warning information includes the warning content and the revised warning level; and The early warning module is used to perform a database early warning according to the target early warning information, the target personnel information and the target early warning notification method.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 6.

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