Data monitoring method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a data monitoring method and device, electronic equipment and storage medium, the method comprising: obtaining a monitoring requirement text through a dialog box; converting the monitoring requirement text into a database monitoring operation through an artificial intelligence module, and obtaining monitoring data from a database based on the database monitoring operation, the artificial intelligence module having previously learned the relationship between historical monitoring requirement texts and historical database monitoring operations; and outputting the monitoring data. Embodiments of the present application can execute monitoring of data based on a monitoring requirement text input by a user in real time, without the need to pre-set monitoring conditions, thereby improving the flexibility of data monitoring, and the corresponding monitoring data can be obtained directly based on the monitoring requirement text input by the user, whereas pre-set monitoring conditions can require triggering of multiple monitoring conditions to obtain the required data, thereby improving the efficiency of data monitoring relative to monitoring based on pre-set monitoring conditions.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data monitoring method, apparatus, electronic device, and storage medium. Background Technology

[0002] In existing technologies, monitoring data in a database typically involves pre-setting certain monitoring conditions and then monitoring the data based on those conditions. Because monitoring is based on these conditions, the required data cannot be directly obtained, resulting in inflexible data monitoring and low monitoring efficiency. Summary of the Invention

[0003] This application provides a data monitoring method, apparatus, electronic device, and storage medium, which helps to improve the flexibility and efficiency of data monitoring.

[0004] To address the aforementioned problems, firstly, embodiments of this application provide a data monitoring method, including:

[0005] Obtain the monitoring request text through the dialog box;

[0006] The monitoring request text is converted into a database monitoring operation by the artificial intelligence module, and monitoring data is obtained from the database based on the database monitoring operation. The artificial intelligence module learns the relationship between historical monitoring request texts and historical database monitoring operations in advance.

[0007] Output the monitoring data.

[0008] Optionally, the monitoring request text includes data query text;

[0009] The step of converting the monitoring request text into database monitoring operations using an artificial intelligence module, and obtaining monitoring data from the database based on the database monitoring operations, includes:

[0010] The artificial intelligence module converts the data query text into a database query statement.

[0011] The monitoring data is retrieved from the database based on the database query statement.

[0012] The output of the monitoring data includes:

[0013] The monitoring data is output to the dialog box.

[0014] Optionally, the monitoring requirement text may also include a data display format;

[0015] Before outputting the monitoring data to the dialog box, the method further includes:

[0016] The monitoring data is organized into the data display format by the artificial intelligence module.

[0017] Optionally, after outputting the monitoring data to the dialog box, the method further includes:

[0018] In response to the data integration request in the dialog box, the monitoring data is integrated into the data dashboard.

[0019] Optionally, the monitoring request text includes an alarm request text;

[0020] The step of converting the monitoring request text into database monitoring operations using an artificial intelligence module, and obtaining monitoring data from the database based on the database monitoring operations, includes:

[0021] The artificial intelligence module converts the alarm request text into alarm configuration conditions for the alarm service, and All The alarm configuration conditions are deployed to the alarm service;

[0022] The alarm service monitors the data in the database based on the alarm configuration conditions, and generates an alarm message as the monitoring data when the data in the database meets the alarm configuration conditions.

[0023] Optional, also includes:

[0024] Obtain the database operation text through the dialog box;

[0025] The artificial intelligence module converts the database operation text into database operation statements, and then performs operations on the data in the database based on the database operation statements.

[0026] Optional, also includes:

[0027] The artificial intelligence module performs anomaly detection on the data in the database and corrects the detected abnormal data.

[0028] Secondly, embodiments of this application provide a data monitoring device, including:

[0029] The monitoring request acquisition module is used to obtain monitoring request text through a dialog box;

[0030] The data monitoring module is used to convert the monitoring request text into database monitoring operations through the artificial intelligence module, and to obtain monitoring data from the database based on the database monitoring operations. The artificial intelligence module learns the relationship between historical monitoring request texts and historical database monitoring operations in advance.

[0031] The data output module is used to output the monitoring data.

[0032] Optionally, the monitoring request text includes data query text;

[0033] The data monitoring module includes:

[0034] The first conversion unit is used to convert the data query text into a database query statement through the artificial intelligence module;

[0035] The data query unit is used to query the monitoring data from the database based on the database query statement;

[0036] The data output module is specifically used for:

[0037] The monitoring data is output to the dialog box.

[0038] Optionally, the monitoring requirement text may also include a data display format;

[0039] The device further includes:

[0040] The data organization module is used to organize the monitoring data into the data display format through the artificial intelligence module.

[0041] Optionally, the monitoring request text includes an alarm request text;

[0042] The data monitoring module includes:

[0043] The second conversion unit is used to convert the alarm request text into alarm configuration conditions for the alarm service through the artificial intelligence module, and to deploy the alarm configuration conditions to the alarm service;

[0044] An alarm monitoring unit is used to monitor the data in the database based on the alarm configuration conditions through the alarm service, and generate an alarm message as the monitoring data when the data in the database meets the alarm configuration conditions.

[0045] Optionally, the device further includes:

[0046] The operation text acquisition module is used to acquire database operation text through the dialog box;

[0047] The database operation module is used to convert the database operation text into database operation statements through the artificial intelligence module, and to perform operations on the data in the database based on the database operation statements.

[0048] Optionally, the device further includes:

[0049] The data correction module is used to detect anomalies in the data in the database through the artificial intelligence module and to correct the detected abnormal data.

[0050] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data monitoring method described in embodiments of this application.

[0051] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to perform the steps of the data monitoring method disclosed in embodiments of this application.

[0052] The data monitoring method, apparatus, electronic device, and storage medium provided in this application embodiment obtain monitoring request text through a dialog box, convert the monitoring request text into database monitoring operations through an artificial intelligence module, and obtain monitoring data from the database based on the database monitoring operations, and output the monitoring data. When performing data monitoring, monitoring can be performed based on the monitoring request text input by the user in real time, without the need to pre-set monitoring conditions, which improves the flexibility of data monitoring. The corresponding monitoring data can be obtained directly based on the monitoring request text input by the user, while pre-set monitoring conditions may require triggering multiple monitoring conditions to obtain the required data. Therefore, the data monitoring efficiency can be improved compared to monitoring with pre-set monitoring conditions. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is an architecture diagram of the data monitoring system in the embodiments of this application;

[0055] Figure 2 This is a flowchart of a data monitoring method provided in an embodiment of this application;

[0056] Figure 3 This is a flowchart of a data monitoring method provided in an embodiment of this application;

[0057] Figure 4 This is a schematic diagram illustrating the implementation of querying and monitoring data in the database in an embodiment of this application;

[0058] Figure 5This is a flowchart of a data monitoring method provided in an embodiment of this application;

[0059] Figure 6 This is a schematic diagram illustrating the implementation of alarm monitoring of data in the database in an embodiment of this application;

[0060] Figure 7 This is a schematic diagram of the structure of a data monitoring device provided in an embodiment of this application;

[0061] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] Figure 1 This is an architecture diagram of the data monitoring system in an embodiment of this application, such as... Figure 1 As shown, the data monitoring system includes a client and a server. The client includes an AI (Artificial Intelligence) dialog box, a data dashboard, and an alarm module (for displaying alarm messages to users). The server includes an AI module, a data acquisition service, a data warehouse, an alarm service, and a UI (User Interface) platform. Existing technologies do not include an AI module and an AI dialog box. Target data is collected from source data sources using data acquisition services such as Sparks, Kafka, and Flink, and stored locally or in a stable cloud platform database, such as StarRocks or MySQL. During data acquisition, the data is cleaned to meet usage requirements. The alarm service pre-sets alarm conditions and matches them against data in the data warehouse. When the conditions are met, an alarm is triggered, and a corresponding alarm message is sent to a pre-defined interface to alert users of any anomalies. The UI platform connects to the database and creates a visual data dashboard. This application embodiment adds an AI dialog box and an AI module. The AI ​​dialog box can receive monitoring request text input by the user. The AI ​​module processes the monitoring request text, converting it into a format suitable for database monitoring operations. After monitoring the database and obtaining monitoring data, the monitoring data is converted into natural language and output. The specific technical solution is as follows:

[0064] Figure 2This is a flowchart illustrating a data monitoring method provided in an embodiment of this application. This data monitoring method can be executed by electronic devices such as servers. Figure 2 As shown, the method includes steps 210 to 230.

[0065] Step 210: Obtain the monitoring requirement text through the dialog box.

[0066] In one exemplary embodiment, the monitoring request text is used to describe the monitoring requirement through text. The monitoring request text can be data query text or alarm request text, and can also be other requirement texts that require data monitoring. The data query text is used to describe the data to be queried, and the alarm request text is used to describe the conditions for alarms through text.

[0067] In one exemplary embodiment, a dialog box can be set up on the client side, where the user can enter monitoring request text. For example, the monitoring request text could be what data needs to be queried, or whether an alarm needs to be triggered for data that meets certain conditions. The server obtains the monitoring request text entered by the user through the dialog box interface with the client.

[0068] In one exemplary embodiment, the dialog box can be visualized in the client using VUE3.

[0069] Step 220: The monitoring request text is converted into a database monitoring operation by the artificial intelligence module, and monitoring data is obtained from the database based on the database monitoring operation.

[0070] The artificial intelligence module learns in advance the relationship between historical monitoring request texts and historical database monitoring operations.

[0071] In one exemplary embodiment, the required database monitoring operations are identified from the monitoring request text, and these operations are described using a database language. Based on these operations, data monitoring is performed on the database, and monitoring data that meets the requirements is retrieved from the database. For example, an artificial intelligence module can be used to convert the monitoring request text into database monitoring operations. The artificial intelligence module may include a large language model or other models to learn in advance the relationship between historical monitoring request texts and historical database monitoring operations to identify the database monitoring operations corresponding to the monitoring request text. The artificial intelligence module can also be pre-connected to the database and learn from the data in the database to acquire the ability to identify abnormal data in the database.

[0072] Step 230: Output the monitoring data.

[0073] In one exemplary embodiment, monitoring data can be output to a dialog box, or it can be output to other corresponding display locations on the client, such as corresponding alarm messages, which can be output to the client in the form of messages.

[0074] The data monitoring method provided in this application obtains monitoring request text through a dialog box, converts the monitoring request text into database monitoring operations through an artificial intelligence module, and obtains monitoring data from the database based on the database monitoring operations, and outputs the monitoring data. When performing data monitoring, monitoring can be performed based on the monitoring request text entered by the user in real time, without the need to pre-set monitoring conditions, which improves the flexibility of data monitoring. The corresponding monitoring data can be obtained directly based on the monitoring request text entered by the user, while pre-set monitoring conditions may require triggering multiple monitoring conditions to obtain the required data. Therefore, the data monitoring efficiency can be improved compared to monitoring with pre-set monitoring conditions.

[0075] Figure 3 This is a flowchart of a data monitoring method provided in an embodiment of this application. This data monitoring method can be executed by electronic devices such as servers. Based on the above embodiments, the monitoring requirement text in this embodiment includes data query text, specifically describing the monitoring of the required data. Figure 3 As shown, the method includes steps 310 to 340.

[0076] Step 310: Obtain the monitoring requirement text through the dialog box.

[0077] The monitoring request text includes data query text.

[0078] Step 320: The data query text is converted into a database query statement through the artificial intelligence module.

[0079] In one exemplary embodiment, the artificial intelligence module can recognize data request text and perform a conversion operation to transform the data request text into a database query statement in database language form.

[0080] In one exemplary embodiment, the artificial intelligence module can use the OpenAI API to convert data query text in natural language form into database query statements, such as SQL statements, supporting the transformation of complex queries. For example, code calling the OpenAI API can take the following form:

[0081] defnatural_language_to_sql(self,query:str)->str:

[0082] self.cursor.execute("SELECT name FROM sqlite_master WHERE type="table';")

[0083] tables=self.cursor.fetchall()

[0084] Step 330: Query the monitoring data from the database based on the database query statement.

[0085] In one exemplary embodiment, data required is retrieved from the database based on a database query statement, serving as monitoring data. The database query statement may utilize database encapsulation functions, and data from the database table structure is obtained based on these encapsulation functions, thus serving as monitoring data.

[0086] Step 340: Output the monitoring data to the dialog box.

[0087] In one exemplary embodiment, monitoring data can be converted into natural language output, and the output can be sent to a dialog box on the client through a dialog interface. When converting monitoring data into natural language output, a natural language conversion function can be invoked to perform the conversion.

[0088] For example, if a user wants to see the number of new questions added to a specific module that day, the AI ​​module can quickly filter the data and output a chart of the corresponding information. This addresses the different chart needs of various users for the Kanban system and overcomes the shortcomings of traditional Kanban systems that only display preset charts.

[0089] The data monitoring method provided in this embodiment converts the data query text into a database query statement through an artificial intelligence module after obtaining the data query text. Based on the database query statement, monitoring data is retrieved from the database. When monitoring data in the database, the corresponding monitoring data can be obtained from the database based on the data query text input by the user. Compared with monitoring based on pre-set monitoring conditions, this method can improve the flexibility and efficiency of data query monitoring.

[0090] Based on the above technical solutions, the monitoring requirement text also includes a data display format;

[0091] Before outputting the monitoring data to the dialog box, the method may further include: organizing the monitoring data into the data display format through the artificial intelligence module.

[0092] In one exemplary embodiment, the monitoring request text entered by the user may include, in addition to the data query text, a data display format, which indicates the display format of the queried monitoring data.

[0093] In one exemplary embodiment, after retrieving the required monitoring data from the database, the monitoring data is organized into a data display format indicated by the user, and then the monitoring data in this data display format can be output to a dialog box.

[0094] By organizing the monitoring data into this display format based on the data presentation format in the monitoring requirement text, it is easier for users to view the data and further improves monitoring efficiency.

[0095] Based on the above technical solution, after outputting the monitoring data to the dialog box, it may further include: responding to the data integration request in the dialog box, integrating the monitoring data into the data dashboard.

[0096] After the monitoring data is output to the dialog box, if the user needs to continue using the monitoring data, they can enter a data integration request in text form in the dialog box. This data integration request is used to request the integration of the monitoring data into the data dashboard. After obtaining the data integration request entered by the user in the client's dialog box through the dialog box interface, the monitoring data is integrated into the data dashboard through the artificial intelligence module, making it convenient for users to view the monitoring data in the data dashboard.

[0097] Figure 4 This is a schematic diagram illustrating the implementation of querying and monitoring data in the database in an embodiment of this application, such as... Figure 4 As shown, the AI ​​module deployed on the cloud server can pre-connect to the database, learn from historical data in the database, and learn the relationship between data query text and database query statements. It can obtain data query text from the dialog box via a dialog interface, input the data query text into the AI ​​module, and the AI ​​module can convert the data query text into a database query statement. Then, it can retrieve the corresponding monitoring data from the database based on this database query statement. The data dashboard service can obtain the corresponding data from the database and form a data dashboard. The AI ​​module can also integrate the monitoring data displayed in the dialog box into the data dashboard based on the data integration request obtained from the dialog box.

[0098] Figure 5 This is a flowchart of a data monitoring method provided in an embodiment of this application. This data monitoring method can be executed by electronic devices such as servers. Based on the above embodiments, the monitoring requirement text in this embodiment includes an alarm requirement text, specifically describing the monitoring of the data requiring alarms. For example... Figure 5As shown, the method includes steps 510 to 540.

[0099] Step 510: Obtain the monitoring requirement text through the dialog box.

[0100] The monitoring request text includes alarm request text.

[0101] Step 520: The alarm request text is converted into alarm configuration conditions for the alarm service through the artificial intelligence module, and the alarm configuration conditions are deployed to the alarm service.

[0102] In an exemplary embodiment, the artificial intelligence module can be linked with the alarm service in advance. After accessing the database, it learns the configuration method of the alarm service's configuration conditions. After obtaining the monitoring request text input by the user through the dialog box, it can identify the alarm configuration conditions in the monitoring request text, and based on the learned configuration conditions of the alarm service, convert the identified alarm configuration conditions into the configuration method of the alarm conditions required in the alarm service, obtain the alarm configuration conditions of the alarm service corresponding to the alarm request text, and deploy the alarm configuration conditions to the alarm service.

[0103] Step 530: The alarm service monitors the data in the database based on the alarm configuration conditions, and generates an alarm message as the monitoring data when the data in the database meets the alarm configuration conditions.

[0104] In one exemplary embodiment, the alarm service can monitor data in the database based on configured alarm configuration conditions. For example, monitoring can be performed while cleaning data in the database or on data that has already been cleaned and stored in the database. If the monitored data meets the alarm configuration conditions, an alarm message corresponding to those conditions is generated, and this alarm message is used as the monitoring data. For example, the alarm configuration conditions could be triggered when data is missing or invalid. Thus, if data meeting the alarm configuration conditions is found during the data cleaning process in the database, an accurate alarm will be triggered.

[0105] Step 540: Output the monitoring data.

[0106] In one exemplary embodiment, the monitoring data is the alarm message, which is output to provide an alert to the user. For example, the alarm message can be output via a pre-defined application's API, or it can be output via email or SMS.

[0107] Figure 6 This is a schematic diagram illustrating the implementation of alarm monitoring of data in the database in this embodiment of the application, such as... Figure 6As shown, the AI ​​module deployed on the cloud server can pre-connect to the database and learn the configuration conditions of the alarm service. Based on the dialog interface, it obtains the alarm request text from the dialog box, inputs the alarm request text into the AI ​​module, and the AI ​​module can convert the alarm request text into the alarm configuration conditions of the alarm service. The alarm configuration conditions are then deployed to the alarm service, which can monitor the data in the database based on the alarm configuration conditions. When the data in the database meets the alarm configuration conditions, an alarm message is generated and output. The alarm message can be output through the API of the preset application.

[0108] The data monitoring method provided in this embodiment, after obtaining the alarm request text, converts the alarm request text into alarm configuration conditions for the alarm service through an artificial intelligence module, and deploys the alarm configuration conditions into the alarm service. The alarm service monitors the data in the database based on the alarm configuration conditions. When the data in the database meets the alarm configuration conditions, an alarm message is generated as monitoring data and output. Thus, when data alarm monitoring is required, the alarm request text input by the user can be converted into alarm configuration conditions for the alarm service in real time and deployed to the alarm service for alarm monitoring. Compared with alarm monitoring based on pre-set alarm monitoring conditions, this method can improve the flexibility of data alarm monitoring.

[0109] Based on the above technical solution, the method may further include: obtaining database operation text through the dialog box; converting the database operation text into database operation statements through an artificial intelligence module; and performing operations on the data in the database based on the database operation statements.

[0110] In an exemplary embodiment, when a user needs to perform operations such as adding, deleting, or modifying data in the database, they can enter the corresponding database operation text in a dialog box on the client. The server obtains the database operation text entered by the user from the dialog box based on the dialog box interface, identifies the database operation text through the artificial intelligence module, determines the database operation to be performed, and converts the database operation text into a database operation statement (e.g., an SQL statement). Based on the database operation statement, the corresponding data in the database is operated.

[0111] By converting the database operation text obtained from the dialog box into database operation statements, it is possible to manipulate the data in the database without requiring the user to input database operation statements, thus improving the flexibility of database operations.

[0112] Based on the above technical solution, the method may further include: performing anomaly detection on the data in the database through the artificial intelligence module, and correcting the detected abnormal data.

[0113] In one exemplary embodiment, an artificial intelligence (AI) module can be provided with the ability to perform CRUD operations on a database. The AI ​​module can learn from a large amount of historical data to identify the forms of anomalous data and their corresponding correct forms. This allows the AI ​​module to detect anomalies in the database and correct the detected anomalous data. For example, historical data can be used to train an anomaly detection model in the AI ​​module. After training, the data to be detected in the database is input into the model. The model then detects anomalies in the data. When an anomaly is detected (such as data loss, invalid data format, or other data anomaly issues), the data can be corrected, and the corresponding data in the database can be updated to the corrected data.

[0114] In one exemplary embodiment, when cleaning data in a database, an artificial intelligence module can be used to detect anomalies in the data to be cleaned and correct the detected abnormal data.

[0115] By using an artificial intelligence module to detect anomalies in the database and correct the detected anomalies, the accuracy of the database data can be improved, thereby improving the accuracy of the monitored data.

[0116] The embodiments of this application can solve the problem of reduced data sensitivity of managers to traditional data dashboards caused by the diversity of dashboard data due to the accumulation of large amounts of data in the prior art. It can obtain the data that we want to pay attention to in real time; at the same time, based on the anomaly detection of data by the artificial intelligence module, abnormal data can be corrected in a timely manner, avoiding inaccurate monitoring results caused by data anomalies, and also avoiding the loss of important alarm information.

[0117] Figure 7 This is a schematic diagram of the structure of a data monitoring device provided in an embodiment of this application, as shown below. Figure 7 As shown, the device includes:

[0118] The monitoring requirement acquisition module 710 is used to acquire monitoring requirement text through a dialog box;

[0119] The data monitoring module 720 is used to convert the monitoring request text into database monitoring operations through the artificial intelligence module, and to obtain monitoring data from the database based on the database monitoring operations. The artificial intelligence module learns in advance the relationship between historical monitoring request texts and historical database monitoring operations.

[0120] The data output module 730 is used to output the monitoring data.

[0121] Optionally, the monitoring request text includes data query text;

[0122] The data monitoring module includes:

[0123] The first conversion unit is used to convert the data query text into a database query statement through the artificial intelligence module;

[0124] The data query unit is used to query the monitoring data from the database based on the database query statement;

[0125] The data output module is specifically used for:

[0126] The monitoring data is output to the dialog box.

[0127] Optionally, the monitoring requirement text may also include a data display format;

[0128] The device further includes:

[0129] The data organization module is used to organize the monitoring data into the data display format through the artificial intelligence module.

[0130] Optionally, the monitoring request text includes an alarm request text;

[0131] The data monitoring module includes:

[0132] The second conversion unit is used to convert the alarm request text into alarm configuration conditions for the alarm service through the artificial intelligence module, and to deploy the alarm configuration conditions to the alarm service;

[0133] An alarm monitoring unit is used to monitor the data in the database based on the alarm configuration conditions through the alarm service, and generate an alarm message as the monitoring data when the data in the database meets the alarm configuration conditions.

[0134] Optionally, the device further includes:

[0135] The operation text acquisition module is used to acquire database operation text through the dialog box;

[0136] The database operation module is used to convert the database operation text into database operation statements through the artificial intelligence module, and to perform operations on the data in the database based on the database operation statements.

[0137] Optionally, the device further includes:

[0138] The data correction module is used to detect anomalies in the data in the database through the artificial intelligence module and to correct the detected abnormal data.

[0139] The data monitoring device provided in this application embodiment is used to implement the steps of the data monitoring method described in this application embodiment. The specific implementation of each module of the device is described in the corresponding steps, and will not be repeated here.

[0140] The data monitoring device provided in this application embodiment obtains monitoring request text through a dialog box, converts the monitoring request text into database monitoring operations through an artificial intelligence module, and obtains monitoring data from the database based on the database monitoring operations, and outputs the monitoring data. When performing data monitoring, monitoring can be performed based on the monitoring request text input by the user in real time, without the need to pre-set monitoring conditions, which improves the flexibility of data monitoring. It can directly obtain the corresponding monitoring data based on the monitoring request text input by the user, while pre-set monitoring conditions may require triggering multiple monitoring conditions to obtain the required data. Therefore, it can improve the efficiency of data monitoring compared to monitoring with pre-set monitoring conditions.

[0141] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 8 As shown, the electronic device 800 may include one or more processors 810 and one or more memories 820 connected to the processors 810. The electronic device 800 may also include an input interface 830 and an output interface 840 for communicating with another device or system. Program code executed by the processor 810 may be stored in the memory 820.

[0142] The processor 810 in the electronic device 800 calls the program code stored in the memory 820 to execute the data monitoring method in the above embodiment.

[0143] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the data monitoring method as described in this application.

[0144] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data monitoring method as described in this application.

[0145] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus embodiments, since they are fundamentally similar to the method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0146] The above provides a detailed description of a data monitoring method, apparatus, electronic device, and storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

Claims

1. A data monitoring method, characterized in that, include: Obtain the monitoring request text through the dialog box; The monitoring request text is converted into a database monitoring operation by the artificial intelligence module, and monitoring data is obtained from the database based on the database monitoring operation. The artificial intelligence module learns the relationship between historical monitoring request texts and historical database monitoring operations in advance. Output the monitoring data.

2. The method according to claim 1, characterized in that, The monitoring request text includes data query text; The step of converting the monitoring request text into database monitoring operations using an artificial intelligence module, and obtaining monitoring data from the database based on the database monitoring operations, includes: The artificial intelligence module converts the data query text into a database query statement. The monitoring data is retrieved from the database based on the database query statement. The output of the monitoring data includes: The monitoring data is output to the dialog box.

3. The method according to claim 2, characterized in that, The monitoring requirement text also includes the data display format; Before outputting the monitoring data to the dialog box, the method further includes: The monitoring data is organized into the data display format by the artificial intelligence module.

4. The method according to claim 2 or 3, characterized in that, After outputting the monitoring data to the dialog box, the method further includes: In response to the data integration request in the dialog box, the monitoring data is integrated into the data dashboard.

5. The method according to any one of claims 1-4, characterized in that, The monitoring request text includes alarm request text; The step of converting the monitoring request text into database monitoring operations using an artificial intelligence module, and obtaining monitoring data from the database based on the database monitoring operations, includes: The artificial intelligence module converts the alarm request text into alarm configuration conditions for the alarm service, and deploys the alarm configuration conditions to the alarm service. The alarm service monitors the data in the database based on the alarm configuration conditions, and generates an alarm message as the monitoring data when the data in the database meets the alarm configuration conditions.

6. The method according to any one of claims 1-5, characterized in that, Also includes: Obtain the database operation text through the dialog box; The artificial intelligence module converts the database operation text into database operation statements, and then performs operations on the data in the database based on the database operation statements.

7. The method according to any one of claims 1-6, characterized in that, Also includes: The artificial intelligence module performs anomaly detection on the data in the database and corrects the detected abnormal data.

8. A data monitoring device, characterized in that, include: The monitoring requirement acquisition module is used to obtain monitoring requirement text through a dialog box; The data monitoring module is used to convert the monitoring request text into database monitoring operations through the artificial intelligence module, and to obtain monitoring data from the database based on the database monitoring operations. The artificial intelligence module learns the relationship between historical monitoring request texts and historical database monitoring operations in advance. The data output module is used to output the monitoring data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the data monitoring method according to any one of claims 1 to 7.

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