Data monitoring and analysis method, device, computer equipment and storage medium

Abstract

The present application relates to the field of artificial intelligence technology, and provides a data monitoring and analysis method, device, computer equipment and storage medium, the method comprising: collecting data volume list information of all hosts and publishing it to the message middleware; obtaining data volume list information, configuring alarm rules for the data volumes of each host contained in the data volume list information based on rule configuration data, and generating alarm rules corresponding to the data volumes of each host; collecting performance data of the data volume of the target host, and storing the performance data in a preset monitoring data center; screening the target alarm rules of the data volume of the target host, and obtaining performance data from the monitoring data center; analyzing the performance data based on the target alarm rules to generate analysis results. The present application can accurately realize effective monitoring and analysis of the performance data of the data volume of the host. The present application can also be applied to the field of blockchain, and the above analysis results and other data can be stored on the blockchain.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and specifically to a data monitoring and analysis method, device, computer equipment and storage medium. Background Art

[0002] With the improvement of computer hardware technology, the improvement of computer interface standards, and the emergence of new systems and technologies, computer architecture has been continuously adjusted so that computer components can perform at their best. Computer hardware architecture has evolved from an all-in-one model to a clustered, storage-computing separation model to meet higher performance requirements, more reliable stability, and better support for distributed, structured deployment requirements.

[0003] With the development and promotion of cloud technology, whether it is a cloud host based on elastic computing or a traditional physical machine that cloud computing relies on, computer resources are shifting towards centralized and large-scale management. In the process of large-scale management of massive computing resources, people currently only pay attention to whether some conventional performance indicators of the host's hardware and software are normal, such as whether the CPU, memory, disk and other resources are sufficient and used reasonably. However, the performance data of the host's data volume that belongs to the dynamically migrated resources will also be related to the host's operating status. However, there is a lack of monitoring of the performance data of the host's data volume, so it is impossible to effectively monitor the business interruption or failure caused by abnormal performance data of the host's data volume. Since it is impossible to analyze and warn the performance data of the host's data volume, it is easy to cause large host operation accidents and economic losses. Therefore, there is an urgent need to provide a processing method that can analyze and warn the performance data of the host's data volume. Summary of the invention

[0004] The main purpose of this application is to provide a data monitoring and analysis method, device, computer equipment and storage medium, aiming to solve the technical problem of the existing lack of a processing method that can analyze and warn the performance data of the host's data volume.

[0005] The present application proposes a data monitoring and analysis method, which comprises the following steps:

[0006] Collecting data volume list information corresponding to all hosts, and publishing the data volume list information to the message middleware;

[0007] Acquire the data volume list information from the message middleware, and perform alarm rule configuration on the data volume of each host contained in the data volume list information based on the input rule configuration data, and generate an alarm rule corresponding to each data volume of the host;

[0008] Collecting performance data of a data volume of a target host, and storing the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts;

[0009] Filtering a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and acquiring the performance data from the monitoring data center;

[0010] The performance data is analyzed and processed based on the target alarm rule to generate analysis results corresponding to the performance data.

[0011] Optionally, the step of configuring alarm rules for each data volume of the host included in the data volume list information based on the input rule configuration data to generate alarm rules corresponding to each data volume of the host includes:

[0012] Determine whether a rule configuration request input by a user is received; wherein the rule configuration request carries user information;

[0013] If the rule configuration request is received, the user is authenticated based on the user information, and whether the authentication is successful is determined;

[0014] If the authority verification is passed, the user's identity is authenticated based on a preset face database and a target verification action corresponding to the user information, and it is determined whether the identity verification is passed;

[0015] If the identity authentication is passed, a preset rule configuration interface is displayed; wherein the rule configuration interface includes the host names of the multiple hosts in the data volume list information;

[0016] Determine a designated host name corresponding to a designated host selected by the user from all the host names, and display a performance indicator corresponding to the designated host and a plurality of condition rule types corresponding to the performance indicator; wherein the designated host name is any one of all the host names;

[0017] Determine a target condition rule type corresponding to the designated host selected by the user from all the condition rule types, and display description information corresponding to the target condition rule type;

[0018] Acquire the target description information selected by the user from the description information and the parameter data input;

[0019] Based on the target description information and the parameter data, a designated alarm rule corresponding to the data volume of the designated host is generated.

[0020] Optionally, the step of performing authority verification on the user based on the user information and determining whether the authority verification is passed includes:

[0021] Obtain the user information, and determine whether the preset permission role list contains the same role information as the user information;

[0022] If the role information is included, obtaining an operation behavior set corresponding to the role information from a preset operation behavior data table;

[0023] Determine whether the operation behavior set includes an alarm rule configuration behavior;

[0024] If the alarm rule configuration behavior is included, obtaining the permission validity date corresponding to the alarm rule configuration behavior;

[0025] Obtain the current time, and determine whether the current time is within the time range corresponding to the validity date of the permission;

[0026] If it is within the time range corresponding to the validity date of the permission, it is determined that the permission verification has passed, otherwise it is determined that the permission verification has failed.

[0027] Optionally, the step of performing identity authentication on the user based on a preset face database and a target authentication action corresponding to the user information, and determining whether the identity authentication is passed, includes:

[0028] Collecting a facial image of the user, and acquiring a target facial image corresponding to the user information from the facial database;

[0029] Determining whether the facial image matches the target facial image;

[0030] If there is a match, randomly selecting a designated verification action from all target verification actions corresponding to the user information; wherein the target verification action includes a head movement and / or a facial expression;

[0031] Generate a prompt message including the action sequence number of the specified verification action, and display the prompt message;

[0032] Collecting the movement trajectory of the user's head when the user moves his head, and obtaining corresponding feedback action information;

[0033] Determining whether the feedback action information matches the specified verification action information corresponding to the specified verification action;

[0034] If they match, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

[0035] Optionally, after the step of generating a designated alarm rule corresponding to the data volume of the designated host based on the target description information and the parameter data, the method further comprises:

[0036] Obtaining a preset mapping table, and querying the importance index corresponding to the designated host based on the mapping table;

[0037] Determining whether the importance index is greater than a preset importance index threshold;

[0038] If it is greater than the importance index threshold, encapsulate the specified alarm rule into a block;

[0039] Storing the blocks through data storage nodes on the blockchain;

[0040] If it is not greater than the importance index threshold, create index information for the specified alarm rule;

[0041] The designated alarm rule is stored in a local preset database based on the index information.

[0042] Optionally, the performance data includes usage of the data volume, usage rate of the data volume, and read and write rate of the data volume; the step of analyzing and processing the performance data based on the target alarm rule to generate an analysis result corresponding to the performance data includes:

[0043] Parsing the target alarm rule to obtain a usage range threshold corresponding to the usage of the data volume, a usage rate range threshold corresponding to the usage rate of the data volume, and a read / write rate range threshold corresponding to the read / write rate of the data volume;

[0044] At the same time, determining whether the usage of the data volume is within the usage range threshold, whether the usage rate of the data volume is within the usage rate range threshold, and whether the read and write rate of the data volume is within the read and write rate range threshold;

[0045] If all data are within the corresponding range threshold, a first analysis result is generated in which the performance data is normal;

[0046] If any data is not within the corresponding range threshold, a second analysis result of abnormal performance data is generated.

[0047] Optionally, the analysis result includes whether the performance data is abnormal or not; after the step of analyzing and processing the performance data based on the target alarm rule to generate the analysis result corresponding to the performance data, the method further includes:

[0048] Determining whether the analysis result indicates that the performance data is abnormal;

[0049] If so, obtaining abnormal data in the performance data;

[0050] Generate corresponding warning information based on the abnormal data;

[0051] Obtaining preset email login information and obtaining a target email address corresponding to a target user;

[0052] Log in to the corresponding mail server according to the mail login information;

[0053] The warning information is sent to the target email address through the email server.

[0054] The present application also provides a data monitoring and analysis device, comprising:

[0055] A first collection module, used for collecting data volume list information corresponding to all hosts, and publishing the data volume list information to the message middleware;

[0056] A configuration module, used to obtain the data volume list information from the message middleware, and perform alarm rule configuration on the data volume of each host contained in the data volume list information based on the input rule configuration data, and generate an alarm rule corresponding to each data volume of the host;

[0057] A second collection module is used to collect performance data of a data volume of a target host and store the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts;

[0058] A screening module, used for screening a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and obtaining the performance data from the monitoring data center;

[0059] The analysis module is used to analyze and process the performance data based on the target alarm rule to generate analysis results corresponding to the performance data.

[0060] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0061] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0062] The data monitoring and analysis method, device, computer equipment and storage medium provided in this application have the following beneficial effects:

[0063] The data monitoring and analysis method, device, computer equipment and storage medium provided in the present application, during the operation of the host, by collecting the data volume list information corresponding to each host, and then configuring the alarm rules for the data volume of each host contained in the data volume list information based on the input rule configuration data, to obtain the alarm rules corresponding to the data volume of each host, and then collect the performance data of the data volume of the target host, and then obtain the target alarm rules corresponding to the data volume of the target host, and analyze and process the performance data based on the target alarm rules, and generate analysis results corresponding to the performance data. Through this embodiment, the performance data of the data volume of the host can be collected in real time, and the automatic configuration of the alarm rules for the data volume of the host can be automatically completed, and then the collected performance data can be analyzed using the corresponding alarm rules, so as to achieve the purpose of effective monitoring and alarming of the data volume of the host, improve the processing intelligence of the performance monitoring of the data volume of the host, make the monitoring and alarming of the dynamic resource of the host data volume more reasonable and efficient, effectively reduce the rule configuration work of the operation and maintenance management personnel, and improve the efficiency of the automatic dynamic configuration of the alarm rules of the data volume. It can also effectively avoid business interruptions caused by the inability to monitor performance indicators of important data objects such as data volumes in abnormal situations such as write failure and data loss when a failure occurs. While improving the level of operation and maintenance, it can effectively avoid economic losses caused by abnormalities and failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flowchart of a data monitoring and analysis method according to an embodiment of the present application;

[0065] Figure 2 It is a structural schematic diagram of a data monitoring and analysis device according to an embodiment of the present application;

[0066] Figure 3 It is a schematic diagram of the structure of a computer device according to an embodiment of the present application.

[0067] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0070] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0071] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0072] Reference Figure 1 , a data monitoring and analysis method according to an embodiment of the present application includes:

[0073] S10: Collecting data volume list information corresponding to all hosts, and publishing the data volume list information to the message middleware;

[0074] S20: Acquire the data volume list information from the message middleware, and perform alarm rule configuration on the data volume of each host contained in the data volume list information based on the input rule configuration data, and generate an alarm rule corresponding to each data volume of the host;

[0075] S30: Collecting performance data of a data volume of a target host, and storing the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts;

[0076] S40: Filtering a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and acquiring the performance data from the monitoring data center;

[0077] S50: Analyze and process the performance data based on the target alarm rule to generate analysis results corresponding to the performance data.

[0078] As described in the above steps S10 to S50, the execution subject of the embodiment of the method is a data monitoring and analysis device. In practical applications, the above data monitoring and analysis device can be implemented by a virtual device, such as software code, or by a physical device with relevant execution code written or integrated, and can interact with the user through a keyboard, mouse, remote control, touchpad or voice control device. The data monitoring and analysis device in this embodiment can realize real-time collection of performance data of the host's data volume, and can automatically complete the automatic configuration of the alarm rules for the host's data volume, and then use the corresponding alarm rules to analyze the collected performance data, so as to achieve the purpose of effective monitoring and alarming of the host's data volume, and improve the processing intelligence of the performance monitoring of the host's data volume. Specifically, first collect the data volume list information corresponding to all hosts, and publish the data volume list information to the message middleware. Among them, the host can be a host in an HA (High Available) cluster. High-availability cluster combined with remote SAN (Storage Area Network) storage is the current mainstream storage and computing separation deployment method, which can meet high availability while ensuring data consistency. The application is deployed on multiple host nodes in the cluster. Each node is connected to the same SAN storage, but only one node remains active and provides services to the outside world. The other nodes serve as standby nodes. When the active node fails, a node is activated in the standby node and called the active node to continue to provide services. When the active node migrates between hosts in the cluster, the SAN storage is mapped to the new active host and becomes the data volume of the active host. When the SAN storage is mapped to different hosts in the cluster, the data volume of the host in the cluster changes. Computer monitoring refers to the process of collecting, analyzing, and alarming performance indicators of its operating system, CPU, memory, disk, network, application, etc. when the computer is running. The data volume monitoring of the host is part of the disk monitoring. By collecting the performance data such as the usage, usage rate, read and write rate of the data volume in real time, the data volume operation and use status is detected to avoid the loss of business data in the case of volume loss, volume fullness, etc. The business scenario of this embodiment can be a scenario of data monitoring of the data volume of the host during the migration of service nodes in a high-availability cluster. In addition, the data volume list information corresponding to all hosts can be collected through a preset volume information collection module and the data volume list information can be published to the message middleware. The volume information collection module can be a functional module generated by the code written by relevant personnel according to actual business usage requirements (such as collecting data volume information of the host). When the volume information collection module is deployed on the host side, the volume information collection module can be used to read the host volume information corresponding to the host. The volume information collection module and the alarm rule configuration center use the message middleware kafka as the data communication medium.The volume information collection module, as the message producer, publishes data to the message middleware Kafka, and uses the preset alarm rule configuration module as the consumer to consume data from the message middleware Kafka. The advantage of this is that it can achieve asynchronous, stable, efficient and decoupled interaction when interacting with massive data.

[0079] Then, the data volume list information is obtained from the message middleware, and the alarm rule configuration is performed on the data volume of each host contained in the data volume list information based on the input rule configuration data, and the alarm rules corresponding to each data volume of the host are generated. Wherein, the rule configuration data includes the target condition rule type, the target description information and the parameter data. In addition, the data volume list information can be obtained from the message middleware through a preset alarm rule configuration module, and the alarm rule configuration is performed on the data volume of each host contained in the data volume list information based on the input rule configuration data, and the alarm rules corresponding to each data volume of the host are generated. The alarm rules generated by the configuration can be stored using a database, such as a relational database or a blockchain, and the monitoring alarm module can provide a query of basic data such as alarm rules. In addition, the monitoring alarm module can be a functional module generated by the code written and generated by relevant personnel according to actual business usage requirements.

[0080] Afterwards, the performance data of the data volume of the target host is collected, and the performance data is stored in the preset monitoring data center. The target host is any one of all the hosts. In addition, the performance data includes data volume usage, usage rate, and read and write rates. By deploying a performance data collection module on the host side, the performance data such as the data volume usage and usage rate of the host are collected using the performance data collection module, and sent to the monitoring data center. The performance data collection module can be a functional module generated by the code written by relevant personnel according to actual business usage requirements. The monitoring data center can use a time series database as a data storage warehouse to meet the data processing requirements of fast reading and storage. The important role of the monitoring data center is: first, receiving the performance data pushed by the performance data collection module; second, providing performance data query services to the monitoring alarm module.

[0081] Subsequently, the target alarm rule corresponding to the data volume of the target host is screened from all the alarm rules, and the performance data is obtained from the monitoring data center. Among them, the alarm rule configuration can be read from the alarm rule configuration module through the preset monitoring alarm module, and the performance data such as the data volume usage rate of the host can be read from the monitoring data center, and the performance data can be further analyzed and processed. When the performance data exceeds the data range threshold in the corresponding alarm rule, an alarm notification can be sent. The monitoring alarm module can be a functional module generated by the code written and generated by relevant personnel according to the actual business usage requirements. Finally, the performance data is analyzed and processed based on the target alarm rule to generate an analysis result corresponding to the performance data. Among them, the specific implementation process of analyzing and processing the performance data based on the target alarm rule and generating the analysis result corresponding to the performance data will be further described in the subsequent specific embodiments of this application, and will not be repeated here. In addition, after the analysis result is generated, it can also be displayed. There is no limitation on the display form of the analysis result, which can be displayed according to actual needs, for example, in text form, voice form, etc.

[0082] In this embodiment, during the operation of the host, by collecting the data volume list information corresponding to each host, and then configuring the alarm rules for the data volumes of each host contained in the data volume list information based on the input rule configuration data, the alarm rules corresponding to the data volumes of each host are obtained, and then the performance data of the data volume of the target host is collected, and then the target alarm rules corresponding to the data volume of the target host are obtained, and the performance data is analyzed and processed based on the target alarm rules to generate analysis results corresponding to the performance data. Through this embodiment, the performance data of the data volume of the host can be collected in real time, and the automatic configuration of the alarm rules for the data volume of the host can be automatically completed, and then the collected performance data is analyzed using the corresponding alarm rules, so as to achieve the purpose of effective monitoring and alarming of the data volume of the host, improve the processing intelligence of the performance monitoring of the data volume of the host, make the monitoring and alarming of the dynamic resource of the host data volume more reasonable and efficient, effectively reduce the rule configuration work of the operation and maintenance management personnel, and improve the efficiency of the automatic dynamic configuration of the alarm rules of the data volume. It can also effectively avoid business interruptions caused by the inability to monitor performance indicators of important data objects such as data volumes in abnormal situations such as write failure and data loss when a failure occurs. While improving the level of operation and maintenance, it can effectively avoid economic losses caused by abnormalities and failures.

[0083] Furthermore, in one embodiment of the present application, the above step S20 includes:

[0084] S200: Determine whether a rule configuration request input by a user is received; wherein the rule configuration request carries user information;

[0085] S201: If the rule configuration request is received, perform authority verification on the user based on the user information, and determine whether the authority verification is passed;

[0086] S202: If the authority verification is passed, the user is authenticated based on a preset face database and a target verification action corresponding to the user information, and it is determined whether the identity verification is passed;

[0087] S203: If the identity authentication is passed, display a preset rule configuration interface; wherein the rule configuration interface includes the host names of the plurality of hosts in the data volume list information;

[0088] S204: Determine a designated host name corresponding to a designated host selected by the user from all the host names, and display a performance indicator corresponding to the designated host and a plurality of condition rule types corresponding to the performance indicator; wherein the designated host name is any one of all the host names;

[0089] S205: Determine a target condition rule type corresponding to the designated host selected by the user from all the condition rule types, and display description information corresponding to the target condition rule type;

[0090] S206: Acquire the target description information selected by the user from the description information and the parameter data input;

[0091] S207: Generate a designated alarm rule corresponding to the data volume of the designated host based on the target description information and the parameter data.

[0092] As described in the above steps S200 to S207, the step of configuring alarm rules for each of the data volumes of the host contained in the data volume list information based on the input rule configuration data, and generating alarm rules corresponding to each of the data volumes of the host, may specifically include: determining whether a rule configuration request input by a user is received. The rule configuration request carries user information. The rule configuration request refers to a request for configuring alarm rules for the data volume of the host triggered by a relevant user. The user information may include the user's name information or the user's ID information. If the rule configuration request is received, the user is authenticated based on the user information, and it is determined whether the authentication is passed. The specific implementation process of authenticating the user based on the user information will be further described in the subsequent specific embodiments, and will not be repeated here. In addition, the user is first authenticated with a relatively simple authentication process. If the authentication fails, the identity authentication process will be directly omitted, and it is determined that the current user does not have the authority to configure the rules, thereby reducing the amount of data processing and improving the intelligence of data processing. If the authority verification is passed, the user is authenticated based on the preset face database and the target verification action corresponding to the user information, and it is determined whether the identity verification is passed. Among them, the specific implementation process of authenticating the user based on the preset face database and the target verification action corresponding to the user information will be further described in the subsequent specific embodiments of this application, and will not be repeated here. If the identity verification is passed, the preset rule configuration interface is displayed. Among them, the rule configuration interface includes the host names of multiple hosts in the data volume list information. Then determine the designated host name corresponding to the designated host selected by the user from all the host names, display the performance index corresponding to the designated host, and multiple conditional rule types corresponding to the performance index. Among them, the designated host name is any one of all the host names. In addition, the performance index at least includes the usage of the data volume, the usage rate of the data volume, and the read and write rate of the data volume. In the process of configuring the alarm rules of the data volume of the host, the alarm rules can be split into performance indicators and conditional rule types, so that the alarm rules can be described and implemented in a programmable manner. Condition rule types may include numerical types (maximum value, minimum value, interval, equal to, greater than, equal to, less than, greater than or equal to, less than or equal to); Boolean (whether true); string (starts at, ends at, regular, contains, length greater than or equal to, length less than, length interval), etc. Then, the target condition rule type corresponding to the specified host selected by the user from all the condition rule types is determined, and the description information corresponding to the target condition rule type is displayed. Subsequently, the target description information selected by the user from the description information and the parameter data input are obtained.Wherein, the description information can also be called an expression, and the range value obtained by combining the parameter data and the description information is the range threshold corresponding to the performance data. Finally, based on the target description information and the parameter data, a specified alarm rule corresponding to the data volume of the specified host is generated. For example, if the performance data is the utilization rate of the data volume, and it is necessary to configure an alarm rule that the utilization rate of the data volume of the host is less than or equal to 60%, the user is required to select a numerical class from multiple conditional rule types, and then select the expression "less than or equal to" from the description information corresponding to the numerical class, and then input the parameter 60%, so that the device can configure the alarm rule that the utilization rate of the data volume of the host is less than or equal to 60% according to the rule configuration data input by the user. In this embodiment, after receiving the rule configuration request input by the user, the user will be authenticated and authenticated. Only when the user passes all the verifications, the rule configuration request input by the user will be responded to later, so as to effectively avoid the situation where the alarm rule function of configuring the data volume of the host is provided to illegal users or unauthorized users, improve the standardization and rationality of the rule configuration request processing, and effectively ensure the configuration security of the alarm rule.

[0093] Furthermore, in an embodiment of the present application, the above step S201 includes:

[0094] S2010: Acquire the user information, and determine whether the preset permission role list contains the same role information as the user information;

[0095] S2011: if the role information is included, obtaining an operation behavior set corresponding to the role information from a preset operation behavior data table;

[0096] S2012: Determine whether the operation behavior set includes an alarm rule configuration behavior;

[0097] S2013: If the alarm rule configuration behavior is included, obtain the permission validity date corresponding to the alarm rule configuration behavior;

[0098] S2014: Obtain the current time, and determine whether the current time is within the time range corresponding to the permission validity date;

[0099] S2015: If it is within the time range corresponding to the permission validity date, it is determined that the permission verification has passed; otherwise, it is determined that the permission verification has not passed.

[0100] As described in the above steps S2010 to S2015, the step of performing authority verification on the user based on the user information and judging whether the authority verification is passed may specifically include: first obtaining the user information, judging whether the preset authority role list contains the same role information as the user information. Wherein, the authority role list is a list data pre-created and stored with users with business authority. If the role information is included, the operation behavior set corresponding to the role information is obtained from the preset operation behavior data table. Wherein, the operation behavior data table is a data table pre-created and stored with role information with business authority, and an operation behavior set corresponding to each role information, and the authority validity date of each operation behavior contained in each operation behavior set. The operation behavior set refers to the behavior information set of the operation behavior that the user corresponding to each role information has the authority to perform. The authority validity date is the time information corresponding to the effective time limit of the operation behavior that the user has the authority to perform. For example, it can be stipulated that a user of a certain identity has the authority to perform certain operation behaviors within a certain time period, or it can be stipulated that the length of time that a user of a certain identity has the operation authority of a certain operation behavior. Then it is judged whether the operation behavior set includes the alarm rule configuration behavior. Among them, if the operation behavior set includes the alarm rule configuration behavior, it can be preliminarily determined that the user has the authority to configure the alarm rules. If the alarm rule configuration behavior is included, the authority validity date corresponding to the alarm rule configuration behavior is obtained. Finally, the current time is obtained, and it is determined whether the current time is within the time range corresponding to the authority validity date. If it is within the time range corresponding to the authority validity date, it is determined that the authority verification has passed, otherwise it is determined that the authority verification has not passed. Among them, if the current time is within the time range corresponding to the authority validity date, it is determined that the user has the authority to configure the alarm rules, and the authority is valid within the current time, then it can be determined that the user has passed the authority verification, and if the authority is invalid within the current time, it can be determined that the user has not passed the authority verification. In this embodiment, after receiving the rule configuration request triggered by the user, it is also necessary to perform authority verification on the user, that is, through multiple verification methods such as authority role list verification, operation behavior set verification, and operation behavior authority validity date verification to complete the user's authority verification processing, thereby improving the accuracy of the authority verification processing. Only when the user has passed all the verifications will the subsequent response to the rule configuration request entered by the user be made, which can effectively avoid the situation where the rule configuration function for the host's data volume is provided to illegal users or unauthorized users, effectively ensure the security of the data volume rule configuration, and improve the standardization of the generation of alarm rules for the host's data volume.

[0101] Furthermore, in an embodiment of the present application, the above step S202 includes:

[0102] S2020: collecting a facial image of the user, and acquiring a target facial image corresponding to the user information from the facial database;

[0103] S2021: Determine whether the facial image matches the target facial image;

[0104] S2022: If a match is found, randomly selecting a designated verification action from all target verification actions corresponding to the user information; wherein the target verification action includes a head movement and / or a facial expression;

[0105] S2023: Generate prompt information including the action sequence number of the specified verification action, and display the prompt information;

[0106] S2024: Collecting a movement trajectory of the user's head when the user moves the head, and obtaining corresponding feedback action information;

[0107] S2025: Determine whether the feedback action information matches the specified verification action information corresponding to the specified verification action;

[0108] S2026: If they match, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

[0109] As described in the above steps S2020 to S2026, the step of authenticating the user based on the preset face database and the target verification action corresponding to the user information and judging whether the identity authentication is passed may specifically include: firstly collecting the face image of the user, and obtaining the target face image corresponding to the user information from the face database. Then judging whether the face image matches the target face image. Wherein, a similarity algorithm, such as a cosine algorithm, may be used to calculate the similarity between the face image and the target face image. If the similarity is greater than a preset similarity threshold, it is judged that the two match. If they match, a designated verification action is randomly selected from all target verification actions corresponding to the user information. Wherein, the target verification action includes head movements and / or facial expressions. Then, a prompt message containing the action sequence number of the designated verification action is generated and displayed. Wherein, the target verification action corresponding to the user information is pre-stored, and in addition, an action sequence number is set for each target verification action in a one-to-one correspondence, and the target verification action and the action sequence number are used to authenticate the user. When it is necessary to authenticate the user, a designated authentication action can be randomly selected from the pre-stored target authentication actions, and then prompt information is generated according to the randomly selected designated authentication action, and the prompt information is provided to the user. The prompt information can be text information containing the action sequence number of the designated authentication action, or it can be voice information for prompting to make the designated authentication action corresponding to the action sequence number. Subsequently, the movement trajectory corresponding to the head of the user when the head moves is collected to obtain the corresponding feedback action information. Finally, it is determined whether the feedback action information matches the designated authentication action information corresponding to the designated authentication action. If it matches, it is determined that the authentication is passed, otherwise it is determined that the authentication is not passed. The data form of the feedback action information can be an image, and the data form of the designated authentication action information is also an image. The similarity between the collected feedback action information and the designated authentication action can be detected. When the similarity is higher than a predetermined value, it is determined that the user's feedback action information matches the designated authentication action information; otherwise, it is determined that the user's feedback action information does not match the designated authentication action information. The mismatch indicates that the user is not doing the authentication at this time or the action performed by the user is incorrect. At this time, it can be determined that the authentication of the user has failed. Optionally, the authentication failure information can also be displayed, and the user is prompted to authenticate again. If the feedback action information obtained by the detection matches the specified verification action information, it means that after providing the prompt information, the user has performed the correct action according to the prompt information. At this time, it can be determined that it is the user himself who is performing identity verification, rather than using a photo (or video) for verification or other users trying to verify, so it can be determined that the user's identity verification has passed.In this embodiment, by randomly providing prompt information to the user, the randomness of the verification action is improved, and the user will be verified only after the user makes the correct verification action, so that the user's participation is required in the verification process, so that even if the criminals steal the user's photos, they cannot pass the verification, thereby achieving the effect of improving the accuracy of identity authentication and ensuring the security of user information.

[0110] Furthermore, in an embodiment of the present application, after the above step S207, the following steps are included:

[0111] S2070: Obtain a preset mapping table, and query the importance index corresponding to the designated host based on the mapping table;

[0112] S2071: Determine whether the importance index is greater than a preset importance index threshold;

[0113] S2072: If it is greater than the importance index threshold, encapsulate the specified alarm rule into a block;

[0114] S2073: Storing the block through a data storage node on the blockchain;

[0115] S2074: If the value is not greater than the importance index threshold, create index information for the specified alarm rule;

[0116] S2075: Storing the designated alarm rule in a local preset database based on the index information.

[0117] As described in the above steps S2070 to S2075, after the step of generating the designated alarm rule corresponding to the data volume of the designated host based on the target description information and the parameter data, a storage process for the designated alarm rule may also be included. Specifically, first obtain a preset mapping table, and query the importance index corresponding to the designated host based on the mapping table. Among them, the mapping table is a data table that stores the association relationship between host information (such as host name) and the importance index in advance according to actual needs. Then determine whether the importance index is greater than the preset importance index threshold. Among them, the value of the importance index threshold is not specifically limited and can be set according to actual needs. If the importance index corresponding to the designated host is greater than the importance index threshold, it means that the importance of the alarm rule corresponding to the data volume of the designated host is high, and the corresponding alarm rule needs to be stored through the blockchain. If the importance index of the designated host is not greater than the importance index threshold, it means that the importance of the alarm rule corresponding to the data volume of the designated host is low, so that only the local preset database needs to be used to store the alarm rule. If it is greater than the importance index threshold, the designated alarm rule is encapsulated as a block. Wherein, the number of blocks is 1 or more. After determining that the importance index of the designated host is greater than the preset importance index threshold based on the mapping table, the alarm rules of the important and critical designated hosts will be intelligently stored in the blockchain, which can effectively prevent the key alarm rules from being tampered with or deleted by the management personnel, and effectively ensure the storage security and intelligence of the alarm rules. After that, the blocks are stored through the data storage nodes on the blockchain. Wherein, after determining that the importance index of the designated host is greater than the preset importance index threshold based on the mapping table, the alarm rules of the data volumes of the important and critical designated hosts will be intelligently stored in the blockchain, which can effectively prevent the key alarm rules from being tampered with or deleted by the management personnel, and effectively ensure the storage security and intelligence of the alarm rules. If it is not greater than the importance index threshold, index information is created for the designated alarm rule. Wherein, by creating index information for the alarm rule, it is convenient to quickly retrieve the alarm rule later. The designated alarm rule is subsequently stored in a local preset database based on the index information. Among them, storing the alarm rules of non-critical and unimportant designated hosts through the local preset database can effectively alleviate the storage pressure of the blockchain to store the alarm rules of important and critical designated hosts, and help to improve the data access rate of the blockchain, improve the processing query efficiency, and reduce the performance requirements and costs of the device equipment, effectively improving the storage intelligence of the alarm rules.In this embodiment, after obtaining the importance index corresponding to the designated host, the alarm rules of the designated host can be adaptively stored according to the importance index corresponding to the designated host, which effectively ensures the storage security of the alarm rules and improves the storage intelligence of the alarm rules.

[0118] Furthermore, in an embodiment of the present application, the performance data includes the usage of the data volume, the usage rate of the data volume, and the read and write rate of the data volume; the above step S50 includes:

[0119] S500: parsing the target alarm rule to obtain a usage range threshold corresponding to the usage of the data volume, a usage rate range threshold corresponding to the usage rate of the data volume, and a read / write rate range threshold corresponding to the read / write rate of the data volume;

[0120] S501: Simultaneously determine whether the usage of the data volume is within the usage range threshold, whether the usage rate of the data volume is within the usage rate range threshold, and whether the read and write rate of the data volume is within the read and write rate range threshold;

[0121] S502: If all data are within the corresponding range threshold, a first analysis result is generated in which the performance data is normal;

[0122] S503: If any data is not within the corresponding range threshold, a second analysis result of abnormal performance data is generated.

[0123] As described in the above steps S500 to S503, the performance data includes the usage of the data volume, the usage rate of the data volume, and the read / write rate of the data volume. The step of analyzing and processing the performance data based on the target alarm rule to generate the analysis result corresponding to the performance data may specifically include: firstly parsing the target alarm rule to obtain the usage range threshold corresponding to the usage of the data volume, the usage range threshold corresponding to the usage rate of the data volume, and the read / write rate range threshold corresponding to the read / write rate of the data volume. Among them, the target alarm rule corresponding to the target host can be obtained, and the usage range threshold, the usage rate range threshold and the read / write rate range threshold are extracted from the configuration information in the target alarm rule. In addition, the usage range threshold, the usage rate range threshold and the read / write rate range threshold are pre-set according to actual business needs, and their specific values ​​are not limited. Then, it is simultaneously determined whether the usage of the data volume is within the usage range threshold, whether the usage rate of the data volume is within the usage range threshold, and whether the read / write rate of the data volume is within the read / write rate range threshold. If all data are within the corresponding range threshold, a first analysis result of normal performance data is generated. If any data is not within the corresponding range threshold, a second analysis result of abnormal performance data is generated. In this embodiment, after obtaining the performance data, the target alarm rule corresponding to the target host can be obtained, and then multiple data range thresholds corresponding to the performance data can be extracted from the target alarm rule, and a one-to-one data comparison process can be performed based on the data range threshold and the performance data. Only when each data included in the performance data is within the corresponding data range threshold will the first analysis result of normal performance data be generated, otherwise the second analysis result of abnormal performance data is generated, which effectively ensures the accuracy of the generated analysis result, and the analysis result can be displayed later, so that relevant users can refer to the displayed analysis result and make corresponding repairs to the host corresponding to the performance data in time, and solve the problems of the host in time, so as to ensure the normal operation of the host.

[0124] Further, in an embodiment of the present application, the above analysis result includes whether the performance data is abnormal or the performance data is not abnormal; after step S50, the following steps are included:

[0125] S510: Determine whether the analysis result indicates that the performance data is abnormal;

[0126] S511: If yes, obtain abnormal data in the performance data;

[0127] S512: Generate corresponding warning information based on the abnormal data;

[0128] S513: Obtaining preset email login information, and obtaining a target email address corresponding to the target user;

[0129] S514: Log in to the corresponding mail server according to the mail login information;

[0130] S515: Send the warning information to the target email address through the email server.

[0131] As described in the above steps S510 to S515, the analysis result includes whether the performance data is abnormal or not. After the step of analyzing and processing the performance data based on the target alarm rule to generate the analysis result corresponding to the performance data, a process of performing early warning analysis based on the obtained analysis result may also be included. Specifically, first determine whether the analysis result is that the performance data is abnormal. Among them, the type of analysis result obtained after analyzing the performance data may include whether the performance data is abnormal or not. If the performance data is abnormal, obtain the abnormal data in the performance data. The abnormal data is the data with abnormality in the performance data, that is, the specific data in the performance data that is not within the corresponding data range threshold. Then generate corresponding early warning information based on the abnormal data. Among them, the early warning information at least includes the abnormal data. A corresponding information template may be pre-set, and then the obtained abnormal data is filled into the corresponding position of the information template, so as to generate the corresponding early warning information. In addition, the specific content of the information template is not limited, for example, it may be: the following abnormal data is found: .... Then obtain the preset email login information, and obtain the target email address corresponding to the target user. Among them, the target user may be a relevant operation and maintenance personnel. Subsequently, the corresponding mail server is logged in according to the email login information. Finally, the warning information is sent to the target email address through the mail server. After generating the analysis results corresponding to the performance data and determining that the analysis results are abnormal in the performance data, this embodiment will intelligently generate corresponding warning information based on the abnormal data in the performance data, and send the warning information to the target email address corresponding to the target user, so that the target user can promptly understand the information that there is data anomaly in the performance data based on the warning information, so as to promptly make corresponding repair processing for the host corresponding to the performance data and solve the problems existing in the host in time, so as to ensure the normal operation of the host.

[0132] The data monitoring and analysis method in the embodiment of the present application can also be applied to the field of blockchain, such as storing the above analysis results and other data on the blockchain. By using blockchain to store and manage the above analysis results, the security and non-tamperability of the above analysis results can be effectively guaranteed.

[0133] The above-mentioned blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0134] The underlying blockchain platform can include processing modules such as user management, basic services, smart contracts, and operation monitoring. Among them, the user management module is responsible for the identity information management of all blockchain participants, including maintaining public and private key generation (account management), key management, and the maintenance of the correspondence between the user's real identity and the blockchain address (authority management), etc., and, under authorization, supervises and audits the transactions of certain real identities and provides risk control rule configuration (risk control audit); the basic service module is deployed on all blockchain node devices to verify the validity of business requests, and records valid requests to storage after consensus is reached. For a new business request, the basic service first adapts the interface for parsing and authentication (interface adaptation), and then encrypts the business information through the consensus algorithm (consensus management). The smart contract module is responsible for the registration and issuance of contracts, as well as contract triggering and contract execution. Developers can define the contract logic in a programming language and publish it to the blockchain (contract registration). According to the logic of the contract terms, the key or other events are called to trigger the execution and complete the contract logic. It also provides the function of contract upgrade and cancellation. The operation monitoring module is mainly responsible for the deployment, configuration modification, contract setting, cloud adaptation and real-time status visualization output of the product during the product release process, such as alarm, network monitoring, node equipment health monitoring, etc.

[0135] Reference Figure 2 In one embodiment of the present application, a data monitoring and analysis device is also provided, including:

[0136] The first acquisition module 1 is used to acquire data volume list information corresponding to all hosts and publish the data volume list information to the message middleware;

[0137] Configuration module 2, used to obtain the data volume list information from the message middleware, and perform alarm rule configuration on the data volume of each host contained in the data volume list information based on the input rule configuration data, and generate alarm rules corresponding to each data volume of the host;

[0138] The second acquisition module 3 is used to collect performance data of the data volume of the target host and store the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts;

[0139] A screening module 4 is used to screen a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and to obtain the performance data from the monitoring data center;

[0140] The analysis module 5 is used to analyze and process the performance data based on the target alarm rule to generate analysis results corresponding to the performance data.

[0141] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0142] Furthermore, in an embodiment of the present application, the configuration module 2 includes:

[0143] A first judgment unit, configured to judge whether a rule configuration request input by a user is received; wherein the rule configuration request carries user information;

[0144] A first verification unit, configured to, upon receiving the rule configuration request, perform authority verification on the user based on the user information, and determine whether the authority verification is passed;

[0145] A second verification unit is used to, if the authority verification is passed, authenticate the user based on a preset face database and a target verification action corresponding to the user information, and determine whether the identity verification is passed;

[0146] A first display unit is used to display a preset rule configuration interface if the identity authentication is passed; wherein the rule configuration interface includes the host names of the plurality of hosts in the data volume list information;

[0147] A second display unit is used to determine a designated host name corresponding to a designated host selected by the user from all the host names, and to display a performance indicator corresponding to the designated host and a plurality of condition rule types corresponding to the performance indicator; wherein the designated host name is any one of all the host names;

[0148] A third display unit is used to determine the target condition rule type corresponding to the designated host selected by the user from all the condition rule types, and to display description information corresponding to the target condition rule type;

[0149] A first acquisition unit, used to acquire target description information selected by the user from the description information and parameter data input;

[0150] The first generating unit is used to generate a designated alarm rule corresponding to the data volume of the designated host based on the target description information and the parameter data.

[0151] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0152] Furthermore, in one embodiment of the present application, the first verification unit includes:

[0153] A first acquisition subunit is used to acquire the user information and determine whether the preset permission role list contains the same role information as the user information;

[0154] A second acquisition subunit is used to acquire an operation behavior set corresponding to the role information from a preset operation behavior data table if the role information is included;

[0155] A first judgment subunit, used to judge whether the operation behavior set includes an alarm rule configuration behavior;

[0156] A third acquisition subunit is used to acquire the permission validity date corresponding to the alarm rule configuration behavior if the alarm rule configuration behavior is included;

[0157] A second judgment subunit is used to obtain the current time and judge whether the current time is within the time range corresponding to the validity date of the permission;

[0158] The first determination subunit is used to determine that the authority verification is passed if the time is within the time range corresponding to the authority validity date, and otherwise determine that the authority verification is not passed.

[0159] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0160] Furthermore, in one embodiment of the present application, the second verification unit includes:

[0161] A first acquisition subunit is used to acquire a facial image of the user and obtain a target facial image corresponding to the user information from the facial database;

[0162] A third judgment subunit, used to judge whether the face image matches the target face image;

[0163] A screening subunit, for randomly screening out a designated verification action from all target verification actions corresponding to the user information if a match is found; wherein the target verification action includes a head action and / or a facial expression;

[0164] A display subunit, used to generate prompt information including the action sequence number of the specified verification action, and display the prompt information;

[0165] The second collecting subunit is used to collect the movement trajectory of the head of the user when the user moves the head, and obtain corresponding feedback action information;

[0166] A fourth judgment subunit, used to judge whether the feedback action information matches the specified verification action information corresponding to the specified verification action;

[0167] The second determination subunit is used to determine that the identity authentication is passed if there is a match, and otherwise determine that the identity authentication is not passed.

[0168] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0169] Furthermore, in an embodiment of the present application, the configuration module 2 includes:

[0170] A second acquisition unit is used to acquire a preset mapping table, and query the importance index corresponding to the designated host based on the mapping table;

[0171] A second judgment unit, used to judge whether the importance index is greater than a preset importance index threshold;

[0172] A first processing unit, configured to encapsulate the specified alarm rule into a block if the value is greater than the importance index threshold;

[0173] A first storage unit, used to store the block through a data storage node on the blockchain;

[0174] A second processing unit, configured to create index information for the specified alarm rule if the value is not greater than the importance index threshold;

[0175] The second storage unit is used to store the specified alarm rule in a local preset database based on the index information.

[0176] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0177] Furthermore, in one embodiment of the present application, the above-mentioned analysis module 5 includes:

[0178] a parsing unit, configured to parse the target alarm rule to obtain a usage range threshold corresponding to the usage of the data volume, a usage rate range threshold corresponding to the usage rate of the data volume, and a read / write rate range threshold corresponding to the read / write rate of the data volume;

[0179] a third judgment unit, configured to simultaneously judge whether the usage of the data volume is within the usage range threshold, whether the usage rate of the data volume is within the usage rate range threshold, and whether the read and write rate of the data volume is within the read and write rate range threshold;

[0180] A second generating unit, configured to generate a first analysis result indicating that the performance data is normal if all the data are within a corresponding range threshold;

[0181] The third generating unit is used to generate a second analysis result of performance data abnormality if any data is not within the corresponding range threshold.

[0182] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0183] Furthermore, in one embodiment of the present application, the data monitoring and analysis device includes:

[0184] A judgment module, used to judge whether the analysis result indicates that the performance data is abnormal;

[0185] A first acquisition module, configured to acquire abnormal data in the performance data;

[0186] A generating module, used for generating corresponding warning information based on the abnormal data;

[0187] The second acquisition module is used to acquire preset email login information and a target email address corresponding to a target user;

[0188] A login module, used to log in to the corresponding mail server according to the mail login information;

[0189] A sending module is used to send the warning information to the target email address through the email server.

[0190] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the data monitoring and analysis method in the aforementioned implementation mode, and will not be repeated here.

[0191] Reference Figure 3 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen, an input device and a database connected through a system bus. Among them, the processor designed for the computer device is used to provide computing and control capabilities. The memory of the computer device includes a storage medium and an internal memory. The storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the storage medium. The database of the computer device is used to store data volume list information, rule configuration data, alarm rules, performance data, target alarm rules and analysis results. The network interface of the computer device is used to communicate with an external terminal through a network connection. The display screen of the computer device is an indispensable graphic output device in the computer, which is used to convert digital signals into optical signals so that text and graphics are displayed on the screen of the display screen. The input device of the computer device is the main device for information exchange between the computer and the user or other devices, and is used to transmit data, instructions and certain flag information to the computer. When the computer program is executed by the processor, a data monitoring and analysis method is implemented.

[0192] The processor executes the following steps of the data monitoring and analysis method:

[0193] Collect data volume list information corresponding to all hosts, and publish the data volume list information to the message middleware; obtain the data volume list information from the message middleware, and configure alarm rules for the data volumes of each of the hosts contained in the data volume list information based on the input rule configuration data, and generate alarm rules corresponding to the data volumes of each of the hosts; collect performance data of the data volumes of the target host, and store the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts; filter the target alarm rules corresponding to the data volumes of the target host from all the alarm rules, and obtain the performance data from the monitoring data center; analyze and process the performance data based on the target alarm rules, and generate analysis results corresponding to the performance data.

[0194] Those skilled in the art will understand that Figure 3 The structure shown in is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the apparatus or computer equipment to which the present application is applied.

[0195] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a data monitoring and analysis method is implemented, specifically:

[0196] Collect data volume list information corresponding to all hosts, and publish the data volume list information to the message middleware; obtain the data volume list information from the message middleware, and configure alarm rules for the data volumes of each of the hosts contained in the data volume list information based on the input rule configuration data, and generate alarm rules corresponding to the data volumes of each of the hosts; collect performance data of the data volumes of the target host, and store the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts; filter the target alarm rules corresponding to the data volumes of the target host from all the alarm rules, and obtain the performance data from the monitoring data center; analyze and process the performance data based on the target alarm rules, and generate analysis results corresponding to the performance data.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0198] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0199] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data monitoring and analysis method, characterized in that: include: Collecting data volume list information corresponding to all hosts, and publishing the data volume list information to the message middleware; Based on the alarm rule configuration module, the data volume list information is obtained from the message middleware, and the alarm rule configuration is performed on the data volume of each host contained in the data volume list information based on the input rule configuration data, and an alarm rule corresponding to each data volume of the host is generated; wherein, the volume information collection module is used to read the data volume list information corresponding to the host, and the volume information collection module publishes data to the message middleware kafka as a message producer, and the preset alarm rule configuration module is used as a consumer to consume data from the message middleware kafka; Collecting performance data of a data volume of a target host, and storing the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts; Filtering a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and acquiring the performance data from the monitoring data center; The performance data is analyzed and processed based on the target alarm rule to generate analysis results corresponding to the performance data.

2. The data monitoring and analysis method according to claim 1, characterized in that: The step of configuring alarm rules for each data volume of the host contained in the data volume list information based on the input rule configuration data to generate alarm rules corresponding to each data volume of the host includes: Determine whether a rule configuration request input by a user is received; wherein the rule configuration request carries user information; If the rule configuration request is received, the user is authenticated based on the user information, and whether the authentication is successful is determined; If the authority verification is passed, the user's identity is authenticated based on a preset face database and a target verification action corresponding to the user information, and it is determined whether the identity verification is passed; If the identity authentication is passed, a preset rule configuration interface is displayed; wherein the rule configuration interface includes the host names of the multiple hosts in the data volume list information; Determine a designated host name corresponding to a designated host selected by the user from all the host names, and display a performance indicator corresponding to the designated host and a plurality of condition rule types corresponding to the performance indicator; wherein the designated host name is any one of all the host names; Determine a target condition rule type corresponding to the designated host selected by the user from all the condition rule types, and display description information corresponding to the target condition rule type; Acquire the target description information selected by the user from the description information and the parameter data input; Based on the target description information and the parameter data, a designated alarm rule corresponding to the data volume of the designated host is generated.

3. The data monitoring and analysis method according to claim 2, characterized in that: The step of performing authority verification on the user based on the user information and determining whether the authority verification is passed includes: Obtain the user information, and determine whether the preset permission role list contains the same role information as the user information; If the role information is included, obtaining an operation behavior set corresponding to the role information from a preset operation behavior data table; Determine whether the operation behavior set includes an alarm rule configuration behavior; If the alarm rule configuration behavior is included, obtaining the permission validity date corresponding to the alarm rule configuration behavior; Obtain the current time, and determine whether the current time is within the time range corresponding to the validity date of the permission; If it is within the time range corresponding to the validity date of the permission, it is determined that the permission verification has passed, otherwise it is determined that the permission verification has failed.

4. The data monitoring and analysis method according to claim 2, characterized in that: The step of authenticating the user based on a preset face database and a target authentication action corresponding to the user information, and determining whether the identity authentication is passed, comprises: Collecting a facial image of the user, and acquiring a target facial image corresponding to the user information from the facial database; Determining whether the facial image matches the target facial image; If there is a match, randomly selecting a designated verification action from all target verification actions corresponding to the user information; wherein the target verification action includes a head movement and / or a facial expression; Generate a prompt message including the action sequence number of the specified verification action, and display the prompt message; Collecting the movement trajectory of the user's head when the user moves his head, and obtaining corresponding feedback action information; Determining whether the feedback action information matches the specified verification action information corresponding to the specified verification action; If they match, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

5. The data monitoring and analysis method according to claim 2, characterized in that: After the step of generating a designated alarm rule corresponding to the data volume of the designated host based on the target description information and the parameter data, the method further comprises: Obtaining a preset mapping table, and querying the importance index corresponding to the designated host based on the mapping table; Determining whether the importance index is greater than a preset importance index threshold; If it is greater than the importance index threshold, encapsulate the specified alarm rule into a block; Storing the blocks through data storage nodes on the blockchain; If it is not greater than the importance index threshold, create index information for the specified alarm rule; The designated alarm rule is stored in a local preset database based on the index information.

6. The data monitoring and analysis method according to claim 1, characterized in that: The performance data includes the usage of the data volume, the usage rate of the data volume, and the read and write rate of the data volume; the step of analyzing and processing the performance data based on the target alarm rule to generate an analysis result corresponding to the performance data includes: Parsing the target alarm rule to obtain a usage range threshold corresponding to the usage of the data volume, a usage rate range threshold corresponding to the usage rate of the data volume, and a read / write rate range threshold corresponding to the read / write rate of the data volume; At the same time, determining whether the usage of the data volume is within the usage range threshold, whether the usage rate of the data volume is within the usage rate range threshold, and whether the read and write rate of the data volume is within the read and write rate range threshold; If all data are within the corresponding range threshold, a first analysis result is generated in which the performance data is normal; If any data is not within the corresponding range threshold, a second analysis result of abnormal performance data is generated.

7. The data monitoring and analysis method according to claim 1, characterized in that: The analysis result includes whether the performance data is abnormal or not abnormal; After the step of analyzing and processing the performance data based on the target alarm rule to generate an analysis result corresponding to the performance data, the method further comprises: Determining whether the analysis result indicates that the performance data is abnormal; If so, obtaining abnormal data in the performance data; Generate corresponding warning information based on the abnormal data; Obtaining preset email login information and obtaining a target email address corresponding to a target user; Log in to the corresponding mail server according to the mail login information; The warning information is sent to the target email address through the email server.

8. A data monitoring and analysis device, used to execute the method according to any one of claims 1 to 7, characterized in that: include: A first collection module, used for collecting data volume list information corresponding to all hosts, and publishing the data volume list information to the message middleware; A configuration module, used to obtain the data volume list information from the message middleware, and perform alarm rule configuration on the data volume of each host contained in the data volume list information based on the input rule configuration data, and generate an alarm rule corresponding to each data volume of the host; A second collection module is used to collect performance data of a data volume of a target host and store the performance data in a preset monitoring data center; wherein the target host is any one of all the hosts; A screening module, used for screening a target alarm rule corresponding to the data volume of the target host from all the alarm rules, and obtaining the performance data from the monitoring data center; The analysis module is used to analyze and process the performance data based on the target alarm rule to generate analysis results corresponding to the performance data.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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