Microservice monitoring method, device, computer equipment and storage medium

Abstract

The present application relates to the field of artificial intelligence technology, and provides a microservice monitoring method, device, computer equipment and storage medium, the method comprising: displaying microservices on a preset service management interface based on a preset container orchestration tool; receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; obtaining user information, the user's handwriting and the user's fingerprint image, and authenticating the user based on the preset answer handwriting and the preset fingerprint model library; if the identity authentication is passed, obtaining the monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction; determining the user's authority score based on the user information, and displaying the monitoring information in the service management interface based on the authority score. The present application can effectively improve the monitoring efficiency and intelligence of microservices. The present application can also be applied to the field of blockchain, and the above-mentioned monitoring information 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 microservice monitoring method, apparatus, computer equipment and storage medium. Background Art

[0002] With the development of microservices, a large number of business units have been extracted to form a single independent microservice, which has led to a large number of microservice instances in a single project. The management, monitoring and operation of microservices have become a bottleneck in the development of small and medium-sized companies. Currently, the monitoring and management of microservices is mainly achieved by system administrators manually maintaining microservices on the server side. This monitoring method requires a lot of time and manpower costs, has low monitoring efficiency, and low intelligence of microservice monitoring, which reduces the user experience. Summary of the invention

[0003] The main purpose of this application is to provide a microservice monitoring method, device, computer equipment and storage medium, aiming to solve the technical problems that the existing microservice monitoring method requires a lot of time and manpower costs, and has low monitoring efficiency and low monitoring intelligence.

[0004] This application proposes a microservice monitoring method, which includes the following steps:

[0005] Based on the preset container orchestration tool, microservices are displayed on the preset service management interface;

[0006] Receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0007] Acquire the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0008] If the identity authentication is passed, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction;

[0009] An authority score of the user is determined based on the user information, and the monitoring information is displayed in the service management interface based on the authority score.

[0010] Optionally, the step of displaying the microservice on a preset service management interface based on a preset container orchestration tool includes:

[0011] Starting the container orchestration tool;

[0012] After the container orchestration tool is successfully started, obtaining a configuration file in the container orchestration tool;

[0013] Calling the preset management tool in the configuration file;

[0014] Based on the preset management tool, the microservice is displayed in the service management interface corresponding to the preset management tool.

[0015] Optionally, the step of obtaining the user information, the user's handwriting and the user's fingerprint image, and authenticating the user based on a preset answer handwriting and a preset fingerprint model library includes:

[0016] Obtaining the user information and obtaining the identity authentication question corresponding to the user information;

[0017] Displaying the identity verification question and preset answer reminder information, so as to remind the user to answer the verification question through the answer reminder information;

[0018] Collecting the handwriting of the answer input by the user at the preset position, and determining whether the content corresponding to the handwriting of the answer is the same as the content of the standard answer to the verification question;

[0019] If they are the same, extracting first handwriting feature data of the answer handwriting, and extracting second handwriting feature data of the preset answer handwriting corresponding to the verification question;

[0020] Determining whether the first handwriting feature data matches the second handwriting feature data;

[0021] If there is a match, acquiring fingerprint images of multiple preset angles corresponding to the target finger of the user, and creating a corresponding first 3D fingerprint model based on all the fingerprint images;

[0022] Filtering a second 3D fingerprint model corresponding to the user information from a preset 3D fingerprint model library, and filtering a third 3D fingerprint model corresponding to the target finger from the second 3D fingerprint model;

[0023] Extracting a first model feature of the first 3D fingerprint model, and extracting a second model feature of the third 3D fingerprint model;

[0024] Calculating a similarity value between the first model feature and the second model feature, and determining whether the similarity value is greater than a preset similarity threshold;

[0025] If it is greater than the similarity threshold, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

[0026] Optionally, the step of acquiring monitoring information of a target microservice corresponding to the target microservice identifier based on the monitoring instruction includes:

[0027] Calling the pre-stored mapping relationship table;

[0028] Query the target microservice corresponding to the target microservice identifier based on the mapping relationship table;

[0029] The operation information of the target microservice is monitored based on the monitoring instruction to obtain monitoring information of the target microservice.

[0030] Optionally, the step of determining the authority score of the user based on the user information, and displaying the monitoring information in the service management interface based on the authority score includes:

[0031] Calling a preset user authority score table, and querying the authority score corresponding to the user information from the user authority score table;

[0032] Determining whether the authority score is within a preset authority score range;

[0033] If it is within the permission score range, displaying the monitoring information in the target area of ​​the service management interface;

[0034] If it is not within the permission score range, the monitoring information is desensitized to obtain the desensitized monitoring information;

[0035] The desensitized monitoring information is displayed in the target area of ​​the service management interface.

[0036] Optionally, the step of performing data desensitization processing on the monitoring information to obtain desensitized monitoring information includes:

[0037] Obtaining a preset sensitive indicator data table; wherein the sensitive indicator data table stores a plurality of sensitive indicators and a level identifier corresponding to each of the sensitive indicators; the level identifier includes a first level identifier, a second level identifier and a third level identifier;

[0038] Obtaining monitoring indicators corresponding to each of the monitoring information;

[0039] Matching each of the monitoring indicators with all the sensitive indicators contained in the sensitive indicator data table, and screening out the indicators to be desensitized from all the monitoring indicators according to the obtained matching results;

[0040] Determine the target level identifier corresponding to each of the indicators to be desensitized based on the sensitive indicator data table;

[0041] Determine the desensitization rules corresponding to each of the indicators to be desensitized based on the target level identifier;

[0042] Based on each of the desensitization rules, desensitization processing is performed on the monitoring indicator parameters associated with each of the indicators to be desensitized one by one to obtain the desensitized monitoring information.

[0043] Optionally, the step of determining the desensitization rules corresponding to each of the indicators to be desensitized respectively based on the target level identifier includes:

[0044] Obtaining a designated level identifier corresponding to a designated indicator to be desensitized; wherein the designated indicator to be desensitized is any one of all the indicators to be desensitized;

[0045] If the designated level identifier is the first level identifier, a first desensitization rule corresponding to the designated indicator to be desensitized is generated based on the first level identifier; wherein the first desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized using a preset encryption method;

[0046] If the designated level identifier is the second level identifier, a second desensitization rule corresponding to the designated indicator to be desensitized is generated based on the second level identifier; wherein the second desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized in a fuzzy manner;

[0047] If the designated level identifier is the third level identifier, a third desensitization rule corresponding to the designated indicator to be desensitized is generated based on the third level identifier; wherein, the third desensitization rule is to perform data desensitization processing on the monitoring indicator parameters corresponding to the designated indicator to be desensitized by replacement.

[0048] The present application also provides a microservice monitoring device, comprising:

[0049] The first display module is used to display the microservice on a preset service management interface based on a preset container orchestration tool;

[0050] A receiving module, used to receive a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0051] A verification module, used to obtain the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0052] An acquisition module, configured to acquire monitoring information of a target microservice corresponding to the target microservice identifier based on the monitoring instruction if the identity authentication is passed;

[0053] A display module is used to determine the authority score of the user based on the user information, and to display the monitoring information in the service management interface based on the authority score.

[0054] 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.

[0055] 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.

[0056] The microservice monitoring method, apparatus, computer device, and storage medium provided in this application have the following beneficial effects:

[0057] The microservice monitoring method, device, computer equipment and storage medium provided in the present application display the microservice on a preset service management interface based on a preset container orchestration tool, then receive the monitoring instruction corresponding to the target microservice identifier triggered by the user in the service management interface, then obtain the user information and authenticate the user, if the identity authentication is passed, then obtain the monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction, finally determine the user's authority score based on the user information, and display the monitoring information in the service management interface based on the authority score. The present application monitors and manages microservices by combining the use of container orchestration tools and service management interfaces, so as to quickly and conveniently implement the monitoring of microservices, with simple and flexible operation, effectively improving the monitoring efficiency and intelligence of microservices, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of a microservice monitoring method according to an embodiment of the present application;

[0059] Figure 2 It is a structural diagram of a microservice monitoring device according to an embodiment of the present application;

[0060] Figure 3 It is a schematic diagram of the structure of a computer device according to an embodiment of the present application.

[0061] 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

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] Reference Figure 1 , a microservice monitoring method according to an embodiment of the present application includes:

[0067] S10: Based on the preset container orchestration tool, the microservices are displayed on the preset service management interface;

[0068] S20: receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0069] S30: Acquire the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0070] S40: If the identity authentication is passed, obtaining monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction;

[0071] S50: Determine the authority score of the user based on the user information, and display the monitoring information in the service management interface based on the authority score.

[0072] As described in the above steps S10 to S50, the execution subject of the embodiment of the method is a microservice monitoring device. In practical applications, the above microservice monitoring 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 microservice monitoring device in this embodiment can monitor and manage microservices by combining the use of container orchestration tools and service management interfaces, effectively improving the monitoring efficiency and monitoring intelligence of microservices, and improving the user experience. Specifically, firstly, the microservice is displayed on the preset service management interface based on the preset container orchestration tool. Among them, the container orchestration tool can provide scheduling and clustering technology, and provide a basic mechanism for scalability based on container applications. The container orchestration tool can use container services and orchestrate containers to determine how containers interact with each other. The container orchestration tool can include any of a variety of types such as dockerswarm, kubernetes, and dockercompose. In addition, for the specific implementation process of displaying microservices on a preset service management interface based on a preset container orchestration tool, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here.

[0073] Then, a monitoring instruction corresponding to the target microservice identifier triggered by the user in the service management interface is received. The target service identifier refers to the identifier corresponding to the target microservice. An information monitoring button corresponding to the target microservice identifier is pre-set in the service management interface. When the user clicks the information monitoring button in the service management interface, the corresponding control instruction, i.e., the monitoring instruction, can be triggered. In addition, the target microservice refers to the microservice controlled by the user. For example, the microservice includes service a, service b, and service c. When the user controls service c, if the monitoring information of service c is obtained, service c is used as the target microservice. In addition, the monitoring instruction can also carry user information. A user information input box is also set at the corresponding position of the information monitoring button, so that the user can enter the corresponding user information in the user information input box. The user information may include information such as user name or user id. After that, the user information, the user's handwriting and the user's fingerprint image are obtained, and the user is authenticated based on the preset answer handwriting and the preset fingerprint model library. Among them, the specific implementation process of authenticating the user based on the preset answer handwriting and the preset fingerprint model library will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here.

[0074] If the identity authentication is passed later, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction. Among them, the pre-stored mapping relationship table can be called first to query the target microservice corresponding to the target microservice identifier, and then the running information of the target microservice is monitored based on the monitoring instruction, so as to obtain the monitoring information of the target microservice. In addition, the monitoring information refers to the indicator parameter information corresponding to the preset monitoring indicator, and the format of the monitoring information includes: monitoring indicator + monitoring indicator parameter. The monitoring indicator can be set according to the actual usage requirements, for example, it can include the call volume, error volume, timeout rate, etc. Finally, the user's authority score is determined based on the user information, and the monitoring information is displayed in the service management interface based on the authority score. Among them, the authority score of the user corresponding to the user information can be queried based on the preset user authority score table. In addition, the acquired monitoring information can be displayed in the target area of ​​the service management interface, so that the monitoring information of the target microservice can be intuitively displayed to the user. The target area refers to the area where the user displays various information of the target microservice, such as monitoring information, log information, etc. In addition, regarding the specific implementation process of displaying the monitoring information in the service management interface based on the authority score, this application will provide further detailed descriptions in subsequent specific embodiments, which will not be elaborated here.

[0075] In this embodiment, the microservice is displayed on a preset service management interface based on a preset container orchestration tool, and then a monitoring instruction corresponding to the target microservice identifier triggered by the user in the service management interface is received, and then the user information is obtained and the user is authenticated. If the identity authentication is passed, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction, and finally the authority score of the user is determined based on the user information, and the monitoring information is displayed in the service management interface based on the authority score. This embodiment combines the use of container orchestration tools and service management interfaces to monitor and manage microservices, so as to quickly and conveniently implement the monitoring of microservices. The operation is simple and flexible, which effectively improves the monitoring efficiency and intelligence of microservices and improves the user experience. In addition, when receiving the monitoring instruction corresponding to the target microservice identifier triggered by the user in the service management interface, the user will also undergo multiple identity authentication related to the preset answer handwriting and the preset fingerprint model library. Only after the user passes the identity authentication will a positive response be made to the monitoring instruction triggered by the user for the target microservice. This can effectively avoid adverse consequences caused by responding to monitoring instructions triggered by illegal users, avoid the leakage of important monitoring information, and ensure the standardization and security of the processing of monitoring instructions.

[0076] Furthermore, in one embodiment of the present application, the above step S10 includes:

[0077] S100: Starting the container orchestration tool;

[0078] S101: After the container orchestration tool is successfully started, obtain a configuration file in the container orchestration tool;

[0079] S102: calling a preset management tool in the configuration file;

[0080] S103: Based on the preset management tool, display the microservice in the service management interface corresponding to the preset management tool.

[0081] As described in steps S100 to S103 above, the step of displaying the microservices on the preset service management interface based on the preset container orchestration tool may specifically include: first starting the container orchestration tool. Among them, the container orchestration tool is preferably docker compose. Docker compose is a docker application that defines and runs multiple containers by a user. The microservices to be managed can be pre-stored in docker, and a docker compose corresponding to docker can be created. Then, after the container orchestration tool is successfully started, the configuration file in the container orchestration tool is obtained. Among them, after starting the container orchestration tool docker compose, a configuration file corresponding to the docker compose can also be established. The preset management tool in the configuration file is subsequently called. Among them, the preset management tool may be a graphical management tool, which refers to an environment for managing an application container engine, and can be composed of any container that can run on an application container engine. In this embodiment, the graphical management tool is preferably portainer. Portainer is a graphical management tool for Docker, which provides status display panel, rapid deployment of application templates, basic operations of container image network data volumes (including uploading and downloading images, creating containers, etc.), event log display, container console operations and services, centralized management and operation, login user management and control, etc. After the configuration file, that is, the dockercompose.yml file, portainer can be embedded in the configuration file, so that portainer can be combined with docker compose, and then after starting dockercompose, a service management interface can be displayed, and the monitoring information of each microservice can be displayed in the service management interface. Finally, based on the preset management tool, the microservice is displayed in the service management interface corresponding to the preset management tool. Among them, the microservice refers to all microservices in the application container engine. Microservice is an emerging software architecture, which refers to splitting a large single application and service into dozens of supporting microservices. A microservice strategy can make work easier, and it can expand a single component instead of the entire application stack to meet the service level agreement. The application container engine refers to an engine that developers can use to package applications and dependency packages into a container. In this embodiment, the application container engine can specifically be a docker container, which is an open source application container engine.Specifically, after starting the container orchestration tool Docker Compose, a configuration file corresponding to Docker Compose is created, and a graphical management tool Portaier is built into the configuration file. The microservices in the application container engine Docker are displayed on a management interface corresponding to the graphical management tool through Portaier, so that the microservices in Docker can be monitored and managed in a visual interface. In this embodiment, the monitoring and management of microservices is performed by combining the use of container orchestration tools and graphical management tools, so that the monitoring of microservices can be quickly and conveniently realized through the service management interface. The operation is simple and flexible, which effectively improves the monitoring efficiency and monitoring intelligence of microservices and improves the user experience.

[0082] Furthermore, in one embodiment of the present application, the above step S30 includes:

[0083] S300: Acquire the user information and the identity authentication question corresponding to the user information;

[0084] S301: Displaying the identity verification question and preset question answering reminder information, so as to remind the user to answer the verification question through the question answering reminder information;

[0085] S302: collecting the handwriting of the answer input by the user at the preset position, and determining whether the content corresponding to the handwriting of the answer is the same as the content of the standard answer to the verification question;

[0086] S303: If they are the same, extracting first handwriting feature data of the answer handwriting, and extracting second handwriting feature data of the preset answer handwriting corresponding to the verification question;

[0087] S304: Determine whether the first handwriting feature data matches the second handwriting feature data;

[0088] S305: If there is a match, obtain fingerprint images of multiple preset angles corresponding to the target finger of the user, and create a corresponding first 3D fingerprint model based on all the fingerprint images;

[0089] S306: Filtering out a second 3D fingerprint model corresponding to the user information from a preset 3D fingerprint model library, and filtering out a third 3D fingerprint model corresponding to the target finger from the second 3D fingerprint model;

[0090] S307: extracting a first model feature of the first 3D fingerprint model, and extracting a second model feature of the third 3D fingerprint model;

[0091] S308: Calculate a similarity value between the first model feature and the second model feature, and determine whether the similarity value is greater than a preset similarity threshold;

[0092] S309: If the similarity is greater than the threshold, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

[0093] As described in the above steps S300 to S309, the step of obtaining the user information, the user's handwriting and the user's fingerprint image, and authenticating the user based on the preset answer handwriting and the preset fingerprint model library may specifically include: first obtaining the user information, and obtaining the identity authentication question corresponding to the user information. The identity authentication question corresponding to the user information may be obtained from a preset question database. The question database is a pre-created database that stores identity authentication questions corresponding to the user information of each legitimate user. The stored identity authentication questions are used to ask questions to the user to implement the identity authentication process for the user. Then the identity authentication question and the preset answer reminder information are displayed to remind the user to answer the verification question through the answer reminder information. There is no specific limitation on the content of the answer reminder information, which can be set according to actual needs, for example, "Please answer the displayed question by handwriting the answer, thank you". Then, the answer handwriting entered by the user at the preset position is collected, and it is determined whether the content corresponding to the answer handwriting is the same as the content of the standard answer to the verification question. The preset position may be a position corresponding to a handwriting input device, and the user may write a feedback answer corresponding to the identity verification question on the handwriting input device of the device, such as the touch screen of the device, so that the device may collect the answer handwriting corresponding to the feedback answer. In addition, the handwriting OCR (Optical Character Recognition) recognition technology may be used to determine whether the content corresponding to the answer handwriting is the same as the content of the standard answer to the identity verification question, that is, the content corresponding to the answer handwriting is first identified according to the handwriting OCR recognition technology, and then it is determined whether the identified content is the same as the content of the standard answer to the identity verification question. If they are the same, the first handwriting feature data of the answer handwriting is extracted, and the second handwriting feature data of the preset answer handwriting corresponding to the verification question is extracted. Then it is determined whether the first handwriting feature data matches the second handwriting feature data. If there is any one of the following situations: the content corresponding to the answer handwriting is inconsistent with the content of the standard answer to the identity verification question, or the first handwriting feature data does not match the second handwriting feature data, it is determined that the user's identity verification has not passed. Only when the content of the answer handwriting is the same as the content of the standard answer to the identity verification question, and the first handwriting feature data matches the second handwriting feature data, will it be preliminarily determined that the user's identity verification has passed. If they match, multiple fingerprint images of preset angles corresponding to the target finger of the user are obtained, and a corresponding first 3D fingerprint model is created based on all the fingerprint images. The fingerprint images of multiple preset angles corresponding to the target finger of the user can be collected by a camera.There is no limitation on the target finger, and it can be set according to actual needs, such as the index finger of the left hand, the thumb of the right hand, and so on. In addition, after the fingerprint image within the acquisition range is collected by the camera, the device will analyze and process the obtained fingerprint image to filter out the fingerprint image of the preset angle. After the fingerprint image is collected by the camera, the angle between the finger and the camera can be determined according to the obtained fingerprint image, that is, after the fingerprint image is collected, the device will analyze and process it, and the result required is the angle between the target finger and the camera. It can be understood that there is no specific limitation on the preset angle, and it can be set according to actual needs. In addition, the process of creating a corresponding first 3D fingerprint model based on all the fingerprint images may include: removing impurities and digitizing the obtained fingerprint image, and the impurity removal may include removing environmental factors, light source factors, etc. other than the fingerprint of the finger in the fingerprint image, and then using 3D modeling technology to establish a first 3D fingerprint model corresponding to the processed fingerprint image. Subsequently, a second 3D fingerprint model corresponding to the user information is filtered out from the preset 3D fingerprint model library, and a third 3D fingerprint model corresponding to the target finger is filtered out from the second 3D fingerprint model. Among them, after obtaining the first 3D fingerprint model corresponding to the target finger of the user, the third 3D fingerprint model corresponding to the user information and the target finger is subsequently intelligently screened from the pre-stored 3D fingerprint model library, and then only the similarity calculation of the features between the first 3D fingerprint model and the third 3D fingerprint model is required, and the feature similarity calculation between the first 3D fingerprint model and all pre-stored 3D fingerprint models is not required, which greatly reduces the amount of data calculation, reduces the device loss, and improves the intelligence of the device data processing. And extract the first model feature of the first 3D fingerprint model, and extract the second model feature of the third 3D fingerprint model. Finally, calculate the similarity value between the first model feature and the second model feature, and judge whether the similarity value is greater than the preset similarity threshold. Among them, a similarity algorithm, such as Han Ming distance, cosine theorem and other algorithms, can be used to calculate the similarity value between the first model feature and the second model feature. In addition, there is no limitation on the specific value of the similarity threshold, which can be set according to actual needs, for example, it can be set to 0.95. If it is greater than the similarity threshold, it is determined that the identity authentication is passed, otherwise it is determined that the identity authentication is not passed. In this embodiment, the accuracy of identity authentication is effectively improved by performing multiple identity authentication of user questions, handwriting verification, and fingerprint model. In addition, only after the user passes the identity authentication will the monitoring instructions for the target microservices input by the user be responded to positively, which can effectively avoid the adverse consequences caused by responding to the monitoring instructions triggered by illegal users, avoid the leakage of important monitoring information, and ensure the processing standardization and security of the monitoring instructions.

[0094] Furthermore, for legitimate users, multiple fingerprint images of preset angles of any one or more fingers can be obtained in sequence, and the 3D fingerprint model of the finger can be established and stored accordingly. This step can be run once or repeatedly until all 3D fingerprint models of all fingers that need to be pre-stored are established and stored. In the process of establishing a 3D model of a finger, it is necessary to obtain multiple fingerprint images of the finger at preset angles in sequence. When all fingerprint images required for modeling are obtained, these fingerprint images are used for modeling; after modeling is completed, an entry cycle can be continued, that is, a new finger can be entered. Repeating multiple times can enter multiple fingers. After the entry is completed, 3D fingerprint models of multiple fingerprints are stored in the device, and all generated 3D fingerprint models can be stored in the 3D fingerprint model library.

[0095] Furthermore, in an embodiment of the present application, the above step S40 includes:

[0096] S400: calling a pre-stored mapping relationship table;

[0097] S401: Query the target microservice corresponding to the target microservice identifier based on the mapping relationship table;

[0098] S402: Monitor the operation information of the target microservice based on the monitoring instruction to obtain monitoring information of the target microservice.

[0099] As described in the above steps S400 to S402, the step of obtaining the monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction may specifically include: first calling a pre-stored mapping relationship table. Wherein, the mapping relationship table is a data table for storing the mapping relationship between the microservice identifier of the microservice and each microservice. After creating and generating a container orchestration tool, while establishing a configuration file corresponding to the container orchestration tool, the mapping relationship table may also be established. Then, the mapping relationship between the microservice and the service identifier is added to the mapping relationship table, and the mapping relationship table after the information is added is stored in the corresponding database. Then, the target microservice corresponding to the target microservice identifier is queried based on the mapping relationship table. Wherein, the microservice identifier is the identifier corresponding to the microservice on the management interface. The microservice identifier refers to the identifier displayed by each microservice on the service management interface, and each microservice corresponds to a unique identifier. For example, the microservices include a, b and c, the identifier corresponding to service a is 1, the identifier corresponding to service b is 2, the identifier corresponding to service c is 3, and so on. Finally, the running information of the target microservice is monitored based on the monitoring instruction to obtain the monitoring information of the target microservice. Among them, by adding the mapping relationship between the microservice and the microservice identifier in the mapping relationship table, when the user performs information monitoring on a microservice identifier, the microservice corresponding to the microservice identifier controlled by the user can be found according to the mapping relationship between the microservice identifier and the microservice to obtain the monitoring information related to the microservice. Specifically, after receiving the monitoring instruction corresponding to the target microservice triggered by the user, the device can monitor the monitoring indicators of the target microservice, so as to obtain the monitoring information of the target microservice. In this embodiment, the target microservice corresponding to the target microservice identifier is quickly queried based on the preset mapping relationship table, and then the operation information of the target microservice can be monitored based on the monitoring instruction to obtain the monitoring information of the target microservice, so that the monitoring information of the target microservice can be conveniently and quickly displayed through the service management interface in the future to complete the monitoring management of the microservice. The operation is simple and flexible, which effectively improves the monitoring efficiency and monitoring intelligence of the microservice and improves the user experience.

[0100] Furthermore, in an embodiment of the present application, the above step S50 includes:

[0101] S500: calling a preset user authority score table, and querying the authority score corresponding to the user information from the user authority score table;

[0102] S501: Determine whether the authority score is within a preset authority score range;

[0103] S502: If the score is within the authority score range, display the monitoring information in a target area of ​​the service management interface;

[0104] S503: If the score is not within the authority score range, desensitize the monitoring information to obtain desensitized monitoring information;

[0105] S504: Displaying the desensitized monitoring information in a target area of ​​the service management interface.

[0106] As described in the above steps S500 to S504, the step of determining the authority score of the user based on the user information and displaying the monitoring information in the service management interface based on the authority score may specifically include: first calling a preset user authority score table, and querying the authority score corresponding to the user information from the user authority score table. Wherein, the user authority score table is a pre-generated table storing user information of legal users and the authority scores corresponding to the user information of each legal user. Then, it is determined whether the authority score is within a preset authority score interval. Wherein, the authority score interval is a score range interval used to determine whether the current user has the right to view the complete monitoring information. If the user's authority score is within the authority score interval, it indicates that the user has the right to view the complete monitoring information, and if the user's authority score is not within the authority score interval, it indicates that the user does not have the right to view the complete monitoring information. At this time, the monitoring information of the target microservice obtained will be intelligently desensitized and then displayed to the user. In addition, there is no limitation on the value of the authority score interval, and it can be set according to actual needs. If it is within the authority score interval, the monitoring information is displayed in the target area of ​​the service management interface. Among them, the target area refers to the area used to display various monitoring information of the target microservice, and the specific location of the target area can be set according to actual needs. By displaying the acquired monitoring information in the target area of ​​the service management interface, the monitoring information of the target microservice can be displayed to the user intuitively, which is conducive to improving the user experience. If it is not within the authority score interval, the monitoring information is subjected to data desensitization processing to obtain the desensitized monitoring information. Among them, the specific implementation process of performing data desensitization processing on the monitoring information to obtain the desensitized monitoring information will be further described in detail in the subsequent specific embodiments of the present application, and will not be elaborated on here. The desensitized monitoring information will be subsequently displayed in the target area of ​​the service management interface. In this embodiment, before displaying the monitoring information of the target microservice, the user's authority score is first obtained and the data display method corresponding to the user is determined based on the authority score. Only when the user's authority score is within the preset authority score range will the monitoring information be directly displayed to the user in its entirety. If the user's authority score is not within the preset authority score range, the monitoring information will be intelligently desensitized before being displayed to the user to avoid leakage of important monitoring indicator information to users who do not have the authority to view the important monitoring indicator information, thereby effectively improving the accuracy and intelligence of viewing the monitoring information.

[0107] Furthermore, in an embodiment of the present application, the above step S503 includes:

[0108] S5030: Obtain a preset sensitive indicator data table; wherein the sensitive indicator data table stores a plurality of sensitive indicators and a level identifier corresponding to each of the sensitive indicators; the level identifier includes a first level identifier, a second level identifier and a third level identifier;

[0109] S5031: Obtain monitoring indicators corresponding to each of the monitoring information;

[0110] S5032: Match each of the monitoring indicators with all the sensitive indicators included in the sensitive indicator data table, and select the indicators to be desensitized from all the monitoring indicators according to the obtained matching results;

[0111] S5033: Determine the target level identifiers corresponding to the indicators to be desensitized based on the sensitive indicator data table;

[0112] S5034: Determine desensitization rules corresponding to each of the indicators to be desensitized based on the target level identifier;

[0113] S5035: Based on each of the desensitization rules, desensitization processing is performed on the monitoring indicator parameters associated with each of the indicators to be desensitized one by one to obtain the desensitized monitoring information.

[0114] As described in steps S5030 to S5035, the step of performing data desensitization processing on the monitoring information to obtain desensitized monitoring information may specifically include:

[0115] First, a preset sensitive indicator data table is obtained. Among them, the sensitive indicator data table stores multiple sensitive indicators and level identifiers corresponding to each of the sensitive indicators. The level identifier includes a first level identifier, a second level identifier and a third level identifier. In addition, the sensitive indicator data table is generated based on the sensitive indicators input by the relevant users. The sensitive indicators are monitoring indicators corresponding to the monitoring information with a higher degree of sensitivity. When the monitoring information of the target microservice is subsequently displayed to users who do not have the complete data content of the monitoring information, it is necessary to perform data desensitization processing on the specified monitoring information corresponding to the sensitive indicator in the monitoring information of the target microservice. And obtain the monitoring indicators corresponding to each of the monitoring information. Among them, the information form of the monitoring information is monitoring indicator + monitoring indicator parameter. Then, each of the monitoring indicators is matched with all the sensitive indicators contained in the sensitive indicator data table, and the indicators to be desensitized are screened out from all the monitoring indicators according to the matching results obtained. Wherein, the screening process of the index to be desensitized may include: matching the monitoring index corresponding to each of the monitoring information with all the sensitive indicators contained in the sensitive indicator data table, and judging whether there is a target indicator identical to the specified monitoring index in the sensitive index; wherein, the specified monitoring index is any one of all the monitoring indicators; if the target indicator exists in the sensitive index, the specified monitoring index is marked as the index to be desensitized, and the index to be desensitized is extracted. In addition, the matching process between the specified monitoring index and all the sensitive indicators can be performed based on a preset parallel comparison instruction, and the above-mentioned parallel comparison instruction can be specifically a single instruction multiple data (SIMD) instruction. By utilizing the parallel computing power of the parallel comparison instruction to simultaneously perform data matching processing on the above-mentioned specified monitoring index and each of the sensitive indicators in all the sensitive indicators, the comparison processing rate between each specified monitoring index and the sensitive indicator is effectively improved, and the acquisition rate of the index to be desensitized is improved. Afterwards, the target level identifier corresponding to each of the indicators to be desensitized is determined based on the sensitive indicator data table. Subsequently, the desensitization rules corresponding to each of the indicators to be desensitized are determined based on the target level identifier. Among them, for the specific implementation process of determining the desensitization rules corresponding to each of the indicators to be desensitized based on the target level identification, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here. Finally, based on each of the desensitization rules, the monitoring indicator parameters associated with each of the indicators to be desensitized are desensitized one by one to obtain the desensitized monitoring information.In this embodiment, based on the sensitive indicator data table, the indicators to be desensitized with higher sensitivity are screened out from all the monitoring indicators corresponding to the monitoring information, and then the target level identifiers corresponding to the indicators to be desensitized are obtained from the sensitive indicator data table, and then the desensitization rules corresponding to the indicators to be desensitized are determined based on the target level identifier, and then the monitoring indicator parameters of the indicators to be desensitized are desensitized one by one using the desensitization rules to obtain the desensitized monitoring information, so as to realize that when it is determined that the user's authority score is not in the preset authority score interval, the monitoring information will be intelligently desensitized before being displayed to the user, so as to avoid the leakage of important monitoring indicator information to users who do not have the authority to view important monitoring indicator information, and effectively improve the accuracy and intelligence of monitoring information viewing. In addition, since only the monitoring indicator parameters corresponding to the indicators to be desensitized in the monitoring information will be desensitized, and not all monitoring information will be desensitized, the unnecessary consumption generated in data processing is effectively reduced, and the intelligence of data desensitization processing is improved.

[0116] Further, the generation process of the sensitive indicator data table may include: first receiving a plurality of pre-input sensitive indicators and sensitivity description information corresponding to each of the sensitive indicators. The sensitive indicators may be set according to actual needs. For example, if the information with a higher sensitivity is a timeout rate, the corresponding sensitive indicator may be a timeout rate indicator. In addition, the sensitivity description information is used to refer to the sensitivity of the sensitive indicator. The user may input the corresponding sensitivity description information for each sensitive indicator according to actual experience. The sensitivity description information may include high sensitivity, medium sensitivity, and low sensitivity. Then, based on the sensitivity description information, a level identifier corresponding to each of the sensitive indicators is generated. The level identifier includes a first level identifier, a second level identifier, and a third level identifier. The level identifier is generated according to the sensitivity description information and is used to identify the sensitivity level of the sensitive indicator. For a sensitive indicator with high sensitivity, a corresponding first level identifier is generated, for a sensitive indicator with medium sensitivity, a corresponding second level identifier is generated, and for a sensitive indicator with low sensitivity, a corresponding third level identifier is generated. Then, a mapping relationship is established between the sensitive indicator and the level identifier and stored in a preset data table to obtain the sensitive indicator data table. In addition, the sensitive indicator data table can be stored in the blockchain. Among them, by using the blockchain to store and manage the sensitive indicator data table, the security and non-tamperability of the sensitive indicator data table can be effectively guaranteed. The sensitive indicator data table is obtained by establishing a corresponding relationship between the sensitive indicator and the level identifier and storing them in a preset data table, which is conducive to the subsequent rapid screening of the indicators to be desensitized from the monitoring indicators corresponding to the monitoring information according to the sensitive indicator data table.

[0117] Furthermore, in an embodiment of the present application, the above step S5034 includes:

[0118] S50340: Obtain a designated level identifier corresponding to a designated indicator to be desensitized; wherein the designated indicator to be desensitized is any one of all the indicators to be desensitized;

[0119] S50341: If the designated level identifier is the first level identifier, generate a first desensitization rule corresponding to the designated indicator to be desensitized based on the first level identifier; wherein the first desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized using a preset encryption method;

[0120] S50342: If the designated level identifier is the second level identifier, generate a second desensitization rule corresponding to the designated indicator to be desensitized based on the second level identifier; wherein the second desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized in a fuzzy manner;

[0121] S50343: If the designated level identifier is the third level identifier, a third desensitization rule corresponding to the designated indicator to be desensitized is generated based on the third level identifier; wherein the third desensitization rule is to perform data desensitization processing on the monitoring indicator parameters corresponding to the designated indicator to be desensitized by replacement.

[0122] As described in the above steps S50340 to S50343, the step of determining the desensitization rules corresponding to each of the indicators to be desensitized based on the target level identifier may specifically include: first obtaining the designated level identifier corresponding to the designated indicator to be desensitized. Wherein, the designated indicator to be desensitized is any one of all the indicators to be desensitized. If the designated level identifier is the first level identifier, a first desensitization rule corresponding to the designated indicator to be desensitized is generated based on the first level identifier. Wherein, for sensitive indicators with higher levels of level identifiers, a desensitization method with higher security will be used for data desensitization. The first desensitization rule is to use a preset encryption method to perform data desensitization on the monitoring indicator parameters corresponding to the designated indicator to be desensitized. In addition, the preset encryption method is not specifically limited, for example, it may include a hash encryption algorithm, an asymmetric encryption algorithm, etc. If the designated level identifier is the second level identifier, a second desensitization rule corresponding to the designated indicator to be desensitized is generated based on the second level identifier. Wherein, the second desensitization rule is to use a fuzzy method to perform data desensitization on the monitoring indicator parameters corresponding to the designated indicator to be desensitized. In addition, the fuzzy method may include a method of processing sensitive data into a fuzzy state that is not easy to view. If the designated level identifier is the third level identifier, a third desensitization rule corresponding to the designated indicator to be desensitized is generated based on the third level identifier. Among them, the third desensitization rule is to use a replacement method to perform data desensitization processing on the monitoring indicator parameters corresponding to the designated indicator to be desensitized. In addition, the replacement method may include methods such as preset character replacement and random character replacement. In this embodiment, for the indicators to be desensitized among the screened monitoring indicators, mutually adapted desensitization rules will be assigned to different sensitive indicators according to the level identifier corresponding to each indicator to be desensitized, so as to achieve the rationality of the desensitization rule configuration, which is conducive to the subsequent data desensitization processing of the monitoring indicator parameters corresponding to each indicator to be desensitized based on the generated desensitization rules to obtain the desensitized monitoring information, thereby ensuring the data standardization and accuracy of the generated desensitized monitoring information.

[0123] The microservice monitoring method in the embodiment of the present application can also be applied to the blockchain field, such as storing the above monitoring information and other data on the blockchain. By using the blockchain to store and manage the above monitoring information, the security and non-tamperability of the above monitoring information can be effectively guaranteed.

[0124] 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.

[0125] 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.

[0126] Reference Figure 2 In one embodiment of the present application, a microservice monitoring device is also provided, including:

[0127] The first display module 1 is used to display the microservice on a preset service management interface based on a preset container orchestration tool;

[0128] Receiving module 2, used to receive a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0129] Verification module 3, used to obtain the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0130] Acquisition module 4, used for acquiring monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction if the identity authentication is passed;

[0131] The second display module 5 is used to determine the authority score of the user based on the user information, and display the monitoring information in the service management interface based on the authority score.

[0132] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0133] Furthermore, in one embodiment of the present application, the first display module 1 includes:

[0134] A startup submodule, used to start the container orchestration tool;

[0135] A first acquisition submodule is used to acquire a configuration file in the container orchestration tool after the container orchestration tool is successfully started;

[0136] A first calling submodule, used for calling a preset management tool in the configuration file;

[0137] The first display submodule is used to display the microservice in the service management interface corresponding to the preset management tool based on the preset management tool.

[0138] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0139] Furthermore, in one embodiment of the present application, the verification module 3 includes:

[0140] A second acquisition submodule is used to acquire the user information and the identity authentication question corresponding to the user information;

[0141] The second display submodule is used to display the identity verification question and preset question answering reminder information, so as to remind the user to answer the verification question through the question answering reminder information;

[0142] A first judgment submodule is used to collect the handwriting of the answer input by the user at a preset position, and judge whether the content corresponding to the handwriting of the answer is the same as the content of the standard answer to the verification question;

[0143] A first extraction submodule, for extracting first handwriting feature data of the answer handwriting if they are the same, and extracting second handwriting feature data of the preset answer handwriting corresponding to the verification question;

[0144] A second judging submodule, used for judging whether the first handwriting feature data matches the second handwriting feature data;

[0145] A third acquisition submodule is used to acquire fingerprint images of multiple preset angles corresponding to the target finger of the user if a match is found, and create a corresponding first 3D fingerprint model based on all the fingerprint images;

[0146] A screening submodule, configured to screen out a second 3D fingerprint model corresponding to the user information from a preset 3D fingerprint model library, and screen out a third 3D fingerprint model corresponding to the target finger from the second 3D fingerprint model;

[0147] A second extraction submodule, configured to extract a first model feature of the first 3D fingerprint model and a second model feature of the third 3D fingerprint model;

[0148] A calculation submodule, used to calculate a similarity value between the first model feature and the second model feature, and determine whether the similarity value is greater than a preset similarity threshold;

[0149] The determination submodule is used to determine that the identity authentication is passed if the similarity is greater than the similarity threshold, otherwise the identity authentication is failed.

[0150] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0151] Furthermore, in an embodiment of the present application, the acquisition module 4 includes:

[0152] The second calling submodule is used to call the pre-stored mapping relationship table;

[0153] A query submodule, used to query the target microservice corresponding to the target microservice identifier based on the mapping relationship table;

[0154] The monitoring submodule is used to monitor the operation information of the target microservice based on the monitoring instruction to obtain the monitoring information of the target microservice.

[0155] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0156] Furthermore, in one embodiment of the present application, the second display module 5 includes:

[0157] A third calling submodule is used to call a preset user authority score table and query the authority score corresponding to the user information from the user authority score table;

[0158] A third judgment submodule is used to judge whether the authority score is within a preset authority score range;

[0159] A third display submodule is used to display the monitoring information in a target area of ​​the service management interface if the information is within the authority score range;

[0160] A processing submodule, for performing data desensitization processing on the monitoring information if the information is not within the authority score range, to obtain desensitized monitoring information;

[0161] The fourth display submodule is used to display the desensitized monitoring information in the target area of ​​the service management interface.

[0162] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0163] Furthermore, in one embodiment of the present application, the processing submodule includes:

[0164] A first acquisition unit is used to acquire a preset sensitive indicator data table; wherein the sensitive indicator data table stores a plurality of sensitive indicators and a level identifier corresponding to each of the sensitive indicators; the level identifier includes a first level identifier, a second level identifier and a third level identifier;

[0165] A second acquisition unit is used to acquire monitoring indicators corresponding to each of the monitoring information;

[0166] A matching unit, used to match each of the monitoring indicators with all the sensitive indicators contained in the sensitive indicator data table, and screen out the indicators to be desensitized from all the monitoring indicators according to the obtained matching results;

[0167] A first determining unit, configured to determine, based on the sensitive indicator data table, target level identifiers corresponding to the indicators to be desensitized;

[0168] A second determining unit, configured to determine, based on the target level identifier, desensitization rules corresponding to each of the indicators to be desensitized;

[0169] The processing unit is used to perform one-to-one desensitization processing on the monitoring indicator parameters associated with each of the indicators to be desensitized based on each of the desensitization rules to obtain the desensitized monitoring information.

[0170] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the microservice monitoring method in the aforementioned implementation mode, and are not repeated here.

[0171] Furthermore, in an embodiment of the present application, the second determining unit includes:

[0172] An acquisition subunit is used to acquire a designated level identifier corresponding to a designated indicator to be desensitized; wherein the designated indicator to be desensitized is any one of all the indicators to be desensitized;

[0173] A first generating subunit is used for generating a first desensitization rule corresponding to the specified indicator to be desensitized based on the first level identifier if the specified level identifier is the first level identifier; wherein the first desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the specified indicator to be desensitized by using a preset encryption method;

[0174] A second generating subunit is used for generating a second desensitization rule corresponding to the specified indicator to be desensitized based on the second level identifier if the specified level identifier is the second level identifier; wherein the second desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the specified indicator to be desensitized in a fuzzy manner;

[0175] The third generating sub-unit is used to generate a third desensitizing rule corresponding to the specified indicator to be desensitized based on the third level identifier if the specified level identifier is the third level identifier; wherein the third desensitizing rule is to perform data desensitization processing on the monitoring indicator parameters corresponding to the specified indicator to be desensitized by replacement.

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

[0177] 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 monitoring instructions, user information, preset answer handwriting, monitoring information and authority scores. 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 sign information to the computer. When the computer program is executed by the processor, a microservice monitoring method is implemented.

[0178] The above processor executes the steps of the above microservice monitoring method:

[0179] Based on the preset container orchestration tool, microservices are displayed on the preset service management interface;

[0180] Receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0181] Acquire the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0182] If the identity authentication is passed, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction;

[0183] An authority score of the user is determined based on the user information, and the monitoring information is displayed in the service management interface based on the authority score.

[0184] 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 present application scheme, and does not constitute a limitation on the devices and computer equipment to which the present application scheme is applied.

[0185] 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 microservice monitoring method is implemented, specifically:

[0186] Based on the preset container orchestration tool, microservices are displayed on the preset service management interface;

[0187] Receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information;

[0188] Acquire the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library;

[0189] If the identity authentication is passed, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction;

[0190] An authority score of the user is determined based on the user information, and the monitoring information is displayed in the service management interface based on the authority score.

[0191] 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).

[0192] 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.

[0193] 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 microservice monitoring method, characterized in that: include: Based on the preset container orchestration tool, microservices are displayed on the preset service management interface; Receiving a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information; Acquire the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on the preset answer handwriting and the preset fingerprint model library; If the identity authentication is passed, the monitoring information of the target microservice corresponding to the target microservice identifier is obtained based on the monitoring instruction; Determining an authority score of the user based on the user information, and displaying the monitoring information in the service management interface based on the authority score; The step of displaying the microservice on a preset service management interface based on a preset container orchestration tool includes: Starting the container orchestration tool; After the container orchestration tool is successfully started, obtaining a configuration file in the container orchestration tool; Calling the preset management tool in the configuration file; Based on the preset management tool, displaying the microservice in the service management interface corresponding to the preset management tool; The step of obtaining the user information, the user's handwriting and the user's fingerprint image, and authenticating the user based on the preset answer handwriting and the preset fingerprint model library includes: Obtaining the user information and obtaining the identity authentication question corresponding to the user information; Displaying the identity verification question and preset answer reminder information, so as to remind the user to answer the verification question through the answer reminder information; Collecting the handwriting of the answer input by the user at the preset position, and determining whether the content corresponding to the handwriting of the answer is the same as the content of the standard answer to the verification question; If they are the same, extracting first handwriting feature data of the answer handwriting, and extracting second handwriting feature data of the preset answer handwriting corresponding to the verification question; Determining whether the first handwriting feature data matches the second handwriting feature data; If there is a match, acquiring fingerprint images of multiple preset angles corresponding to the target finger of the user, and creating a corresponding first 3D fingerprint model based on all the fingerprint images; Filtering a second 3D fingerprint model corresponding to the user information from a preset 3D fingerprint model library, and filtering a third 3D fingerprint model corresponding to the target finger from the second 3D fingerprint model; Extracting a first model feature of the first 3D fingerprint model, and extracting a second model feature of the third 3D fingerprint model; Calculating a similarity value between the first model feature and the second model feature, and determining whether the similarity value is greater than a preset similarity threshold; If it is greater than the similarity threshold, the identity authentication is determined to be successful; otherwise, the identity authentication is determined to be unsuccessful.

2. The microservice monitoring method according to claim 1, characterized in that: The step of acquiring monitoring information of the target microservice corresponding to the target microservice identifier based on the monitoring instruction includes: Calling the pre-stored mapping relationship table; Query the target microservice corresponding to the target microservice identifier based on the mapping relationship table; The operation information of the target microservice is monitored based on the monitoring instruction to obtain monitoring information of the target microservice.

3. The microservice monitoring method according to claim 1, characterized in that: The step of determining the authority score of the user based on the user information, and displaying the monitoring information in the service management interface based on the authority score includes: Calling a preset user authority score table, and querying the authority score corresponding to the user information from the user authority score table; Determining whether the authority score is within a preset authority score range; If it is within the permission score range, displaying the monitoring information in the target area of ​​the service management interface; If it is not within the permission score range, the monitoring information is desensitized to obtain the desensitized monitoring information; The desensitized monitoring information is displayed in the target area of ​​the service management interface.

4. The microservice monitoring method according to claim 3, characterized in that: The step of performing data desensitization processing on the monitoring information to obtain desensitized monitoring information includes: Obtaining a preset sensitive indicator data table; wherein the sensitive indicator data table stores a plurality of sensitive indicators and a level identifier corresponding to each of the sensitive indicators; the level identifier includes a first level identifier, a second level identifier and a third level identifier; Obtaining monitoring indicators corresponding to each of the monitoring information; Matching each of the monitoring indicators with all the sensitive indicators contained in the sensitive indicator data table, and screening out the indicators to be desensitized from all the monitoring indicators according to the obtained matching results; Determine the target level identifier corresponding to each of the indicators to be desensitized based on the sensitive indicator data table; Determine the desensitization rules corresponding to each of the indicators to be desensitized based on the target level identifier; Based on each of the desensitization rules, desensitization processing is performed on the monitoring indicator parameters associated with each of the indicators to be desensitized one by one to obtain the desensitized monitoring information.

5. The microservice monitoring method according to claim 4, characterized in that: The step of determining the desensitization rules corresponding to each of the indicators to be desensitized based on the target level identifier includes: Obtaining a designated level identifier corresponding to a designated indicator to be desensitized; wherein the designated indicator to be desensitized is any one of all the indicators to be desensitized; If the designated level identifier is the first level identifier, a first desensitization rule corresponding to the designated indicator to be desensitized is generated based on the first level identifier; wherein the first desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized using a preset encryption method; If the designated level identifier is the second level identifier, a second desensitization rule corresponding to the designated indicator to be desensitized is generated based on the second level identifier; wherein the second desensitization rule is to perform data desensitization processing on the monitoring indicator parameter corresponding to the designated indicator to be desensitized in a fuzzy manner; If the designated level identifier is the third level identifier, a third desensitization rule corresponding to the designated indicator to be desensitized is generated based on the third level identifier; wherein, the third desensitization rule is to perform data desensitization processing on the monitoring indicator parameters corresponding to the designated indicator to be desensitized by replacement.

6. A microservice monitoring device, characterized in that: The method for implementing the microservice monitoring method according to any one of claims 1 to 5 comprises: The first display module is used to display the microservice on a preset service management interface based on a preset container orchestration tool; A receiving module, used to receive a monitoring instruction corresponding to a target microservice identifier triggered by a user in the service management interface; wherein the monitoring instruction carries user information; A verification module, used to obtain the user information, the user's handwriting and the user's fingerprint image, and authenticate the user based on a preset answer handwriting and a preset fingerprint model library; An acquisition module, configured to acquire monitoring information of a target microservice corresponding to the target microservice identifier based on the monitoring instruction if the identity authentication is passed; The second display module is used to determine the authority score of the user based on the user information, and display the monitoring information in the service management interface based on the authority score.

7. 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 5 are implemented.

8. 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 5 are implemented.

Images