Multi-type data off-line autonomous method for computer network host management platform

Abstract

The invention particularly relates to a multi-type data off-line autonomous method for a computer network host management platform. According to the multi-type data off-line autonomous method for the computing network host management platform, various types of data are collected on the computing network host management platform, are stored locally and are classified and preprocessed; making a corresponding offline autonomous strategy for each type of data, executing a data processing task, and realizing autonomous management and analysis of the data; and an offline processing result is fed back to the computing network host management platform, and an offline autonomous strategy is optimized and adjusted, so that the efficiency and the accuracy of data processing are improved. According to the multi-type data offline autonomy method for the computing network host management platform, the autonomous operation and management capability of multi-type data can be maintained under the condition that network connection is interrupted or unstable, the stability and reliability of a system are improved, the degree of dependence on an external network environment is reduced, and the user experience is improved. And the continuity and availability of the whole service are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data governance, and in particular to an offline autonomous method for multiple types of data for a computing network host management platform. Background Art

[0002] With the rapid development of cloud computing technology, virtualization technology has been widely used in host management platforms. Cloud host management platforms can integrate computing, storage, and network resources to provide users with efficient, flexible, and scalable IT infrastructure services. This technical background makes terminal management no longer limited to traditional physical devices, but extends to virtualized cloud environments.

[0003] In the computing network host management platform, processing multiple types of data (such as device lists, user information, business light application data, etc.) is the key to platform operation. However, the complexity and uncertainty of the network environment may cause connection interruptions, affecting the real-time processing and management of data. Therefore, achieving offline autonomy of multiple types of data has become an important means to improve system stability and reliability.

[0004] In order to ensure that in a computing network host management platform, multiple types of data can still maintain autonomous operation and management even when the network connection is interrupted or unstable, the present invention proposes an offline autonomous method for multiple types of data for a computing network host management platform. Summary of the invention

[0005] In order to make up for the defects of the prior art, the present invention provides a simple and efficient offline autonomous method for multiple types of data for a computing network host management platform.

[0006] The present invention is achieved through the following technical solutions:

[0007] A multi-type data offline autonomous method for a computing network host management platform, characterized by comprising the following steps:

[0008] Step S1: Data collection

[0009] In the computing network host management platform, various types of data are automatically identified and collected, including user information, computing network host device information, device light application information, business light application information and user behavior data, and the collected information is stored locally;

[0010] Analyze the real-time nature of the data, distinguish between real-time data and offline processed data, and only include offline processed data in subsequent steps;

[0011] Step S2: Data classification and preprocessing

[0012] Classify and process various types of collected data, and perform necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability;

[0013] Step S3: Offline autonomous strategy formulation

[0014] According to preset autonomous rules or intelligent algorithms obtained from historical data analysis, formulate corresponding offline autonomous strategies for each type of data, including data storage strategy, analysis algorithm selection and execution plan;

[0015] Step S4: Offline processing execution

[0016] According to the established offline autonomous strategy, data processing tasks are performed outside the computing network host management platform or in a designated offline environment to achieve autonomous management and analysis of data;

[0017] Step S5: Result feedback and optimization

[0018] The offline processing results are fed back to the computing network host management platform, and the offline autonomous strategy is optimized and adjusted according to the processing effect to improve the efficiency and accuracy of data processing.

[0019] In step S1, a suitable local storage solution is selected to store multiple types of data:

[0020] Use direct-attached storage (DAS), network-attached storage (NAS) and / or storage area network (SAN) to store general data including user information and user behavior data;

[0021] Use SQLite database to store complex data including computing network host device information, device light application information and business light application information;

[0022] Complex data including computing network host device information, device light application information and business light application information are downloaded to the local storage through the Android or iOS platform in the form of bundle package data.

[0023] In step S1, before the data is stored locally, the collected data is encrypted using an encryption algorithm to ensure data security, especially for key data such as user information, which needs to be encrypted and stored. The AES symmetric encryption algorithm is used, and the encryption and decryption functions are realized with the help of the corresponding library.

[0024] In step S2, a corresponding data model is designed according to the type of data, and an algorithm is used to automatically classify and preprocess the data to improve processing efficiency and accuracy;

[0025] Design a single data model for single entity data including user information;

[0026] For the computing network host device information, device light application information, and business light application information, tables or Realm models are designed respectively.

[0027] In step S4, based on the Android and Ios platforms, corresponding network monitoring means are used to monitor the network status in real time so that data can be automatically uploaded when the network is restored.

[0028] At the same time, an offline data queue is designed. Whenever there is data to be uploaded but the network fails, the corresponding data is added to the offline data queue;

[0029] Specifically, a table is created in the local database, which contains data content, timestamp and status fields for storing data records to be uploaded; the status field includes "not uploaded" and "uploaded";

[0030] When it is detected in real time that the network connection is restored, the data to be uploaded is read from the offline data queue and uploaded to the server one by one or in batches;

[0031] After the upload is successful, the status of the data record is updated to uploaded, and the corresponding data is removed from the offline data queue.

[0032] In step S4, when uploading data, a token-based identity authentication mechanism (such as JWT) is used for authentication;

[0033] After the user logs in successfully, the server returns a token, and the client attaches the token to the HTTP request header every time it uploads data.

[0034] In step S4, Redux is used for state management, and redux-offline middleware is used to process offline data.

[0035] The redux-offline middleware monitors each action data change, serializes the data that needs to be offline to the local, and restores the data from the local to the Redux store when the page is refreshed.

[0036] In step S4, when data is updated or synchronized, the corresponding library is used to unify the data structure and flatten the nested data, so that the data structure is clearer and easier to manage, which facilitates the storage, update and transmission of data.

[0037] A multi-type data offline autonomous system for a computing network host management platform, comprising:

[0038] The data collection module is responsible for automatically identifying and collecting various types of data in the computing network host management platform, including user information, computing network host device information, device light application information, business light application information and user behavior data, and storing the collected information locally;

[0039] At the same time, the real-time nature of the data is analyzed to distinguish between real-time data and offline processed data, and only offline processed data is included in subsequent steps;

[0040] The data classification and preprocessing module is responsible for classifying and processing various types of collected data, and performing necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability;

[0041] The offline autonomous strategy formulation module is responsible for formulating corresponding offline autonomous strategies for each type of data based on preset autonomous rules or intelligent algorithms obtained based on historical data analysis, including data storage strategies, analysis algorithm selection, and execution plans;

[0042] The offline processing execution module is responsible for executing data processing tasks outside the computing network host management platform or in a designated offline environment in accordance with the established offline autonomous strategy, thus realizing autonomous management and analysis of data.

[0043] The result feedback and optimization module is responsible for feeding back the offline processing results to the computing network host management platform, and optimizing and adjusting the offline autonomous strategy according to the processing effect to improve the efficiency and accuracy of data processing.

[0044] It also includes a monitoring and alarm module, which is used to monitor the execution status of offline processing tasks in real time and automatically trigger the alarm mechanism when an abnormality is found.

[0045] A computing device, comprising:

[0046] One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0047] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method when executed by a processor.

[0048] The beneficial effect of the present invention is that the offline autonomous method for multiple types of data used in the computing network host management platform can maintain the autonomous operation and management capabilities of multiple types of data when the network connection is interrupted or unstable, which not only improves the stability and reliability of the system, but also reduces the dependence on the external network environment, and improves the continuity and availability of the overall business. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Attached Figure 1 It is a schematic diagram of the offline autonomous method for multiple types of data used in a computing network host management platform according to the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] The multi-type data offline autonomous method for a computing network host management platform comprises the following steps:

[0053] Step S1: Data collection

[0054] In the computing network host management platform, various types of data are automatically identified and collected, including user information, computing network host device information, device light application information, business light application information and user behavior data, and the collected information is stored locally;

[0055] Analyze the real-time nature of the data, distinguish between real-time data and offline processed data, and only include offline processed data in subsequent steps;

[0056] Step S2: Data classification and preprocessing

[0057] Classify and process various types of collected data, and perform necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability;

[0058] Step S3: Offline autonomous strategy formulation

[0059] According to preset autonomous rules or intelligent algorithms obtained from historical data analysis, formulate corresponding offline autonomous strategies for each type of data, including data storage strategy, analysis algorithm selection and execution plan;

[0060] Step S4: Offline processing execution

[0061] According to the established offline autonomous strategy, data processing tasks are performed outside the computing network host management platform or in a designated offline environment to achieve autonomous management and analysis of data;

[0062] Step S5: Result feedback and optimization

[0063] The offline processing results are fed back to the computing network host management platform, and the offline autonomous strategy is optimized and adjusted according to the processing effect to improve the efficiency and accuracy of data processing.

[0064] In step S1, a suitable local storage solution is selected to store multiple types of data:

[0065] Use direct-attached storage (DAS), network-attached storage (NAS) and / or storage area network (SAN) to store general data including user information and user behavior data;

[0066] Use SQLite database to store complex data including computing network host device information, device light application information and business light application information;

[0067] Complex data including computing network host device information, device light application information and business light application information are downloaded to the local storage through the Android or iOS platform in the form of bundle package data.

[0068] In step S1, before the data is stored locally, the collected data is encrypted using an encryption algorithm to ensure data security, especially for key data such as user information, which needs to be encrypted and stored. The AES symmetric encryption algorithm is used, and the encryption and decryption functions are realized with the help of the corresponding library.

[0069] In step S2, a corresponding data model is designed according to the type of data, and an algorithm is used to automatically classify and preprocess the data to improve processing efficiency and accuracy;

[0070] Design a single data model for single entity data including user information;

[0071] For the computing network host device information, device light application information, and business light application information, tables or Realm models are designed respectively.

[0072] In step S4, based on the Android and Ios platforms, corresponding network monitoring means are used to monitor the network status in real time so that data can be automatically uploaded when the network is restored.

[0073] At the same time, an offline data queue is designed. Whenever there is data to be uploaded but the network fails, the corresponding data is added to the offline data queue;

[0074] Specifically, a table is created in the local database, which contains data content, timestamp and status fields for storing data records to be uploaded; the status field includes "not uploaded" and "uploaded";

[0075] When it is detected in real time that the network connection is restored, the data to be uploaded is read from the offline data queue and uploaded to the server one by one or in batches;

[0076] After the upload is successful, the status of the data record is updated to uploaded, and the corresponding data is removed from the offline data queue.

[0077] In step S4, when uploading data, a token-based identity authentication mechanism (such as JWT) is used for authentication;

[0078] After the user logs in successfully, the server returns a token, and the client attaches the token to the HTTP request header every time it uploads data.

[0079] In step S4, Redux is used for state management, and redux-offline middleware is used to process offline data.

[0080] The redux-offline middleware monitors each action data change, serializes the data that needs to be offline to the local, and restores the data from the local to the Redux store when the page is refreshed.

[0081] In step S4, when data is updated or synchronized, the corresponding library is used to unify the data structure and flatten the nested data, so that the data structure is clearer and easier to manage, which facilitates the storage, update and transmission of data.

[0082] The multi-type data offline autonomous system for the computing network host management platform includes:

[0083] The data collection module is responsible for automatically identifying and collecting various types of data in the computing network host management platform, including user information, computing network host device information, device light application information, business light application information and user behavior data, and storing the collected information locally;

[0084] At the same time, the real-time nature of the data is analyzed to distinguish between real-time data and offline processed data, and only offline processed data is included in subsequent steps;

[0085] The data classification and preprocessing module is responsible for classifying and processing various types of collected data, and performing necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability;

[0086] The offline autonomous strategy formulation module is responsible for formulating corresponding offline autonomous strategies for each type of data based on preset autonomous rules or intelligent algorithms obtained based on historical data analysis, including data storage strategies, analysis algorithm selection, and execution plans;

[0087] The offline processing execution module is responsible for executing data processing tasks outside the computing network host management platform or in a designated offline environment in accordance with the established offline autonomous strategy, thus realizing autonomous management and analysis of data.

[0088] The result feedback and optimization module is responsible for feeding back the offline processing results to the computing network host management platform, and optimizing and adjusting the offline autonomous strategy according to the processing effect to improve the efficiency and accuracy of data processing.

[0089] It also includes a monitoring and alarm module, which is used to monitor the execution status of offline processing tasks in real time and automatically trigger the alarm mechanism when an abnormality is found.

[0090] The computer-readable storage medium storing one or more programs is characterized in that: the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0091] The computing device is characterized by comprising:

[0092] One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0093] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-type data offline autonomous method for a computing network host management platform, characterized by: The following steps are involved: Step S1: Data collection In the computing network host management platform, various types of data are automatically identified and collected, including user information, computing network host device information, device light application information, business light application information and user behavior data, and the collected information is stored locally; Analyze the real-time nature of the data, distinguish between real-time data and offline processed data, and only include offline processed data in subsequent steps; Step S2: Data classification and preprocessing Classify and process various types of collected data, and perform necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability; Step S3: Offline autonomous strategy formulation According to preset autonomous rules or intelligent algorithms obtained from historical data analysis, formulate corresponding offline autonomous strategies for each type of data, including data storage strategy, analysis algorithm selection and execution plan; Step S4: Offline processing execution According to the established offline autonomous strategy, data processing tasks are performed outside the computing network host management platform or in a designated offline environment to achieve autonomous management and analysis of data; Step S5: Result feedback and optimization The offline processing results are fed back to the computing network host management platform, and the offline autonomous strategy is optimized and adjusted according to the processing effect to improve the efficiency and accuracy of data processing.

2. The multi-type data offline autonomous method for a computing network host management platform according to claim 1 is characterized by: In step S1, a suitable local storage solution is selected to store multiple types of data: Use direct-attached storage DAS, network-attached storage NAS and / or storage area network SAN to store regular data including user information and user behavior data; Use SQLite database to store complex data including computing network host device information, device light application information and business light application information; Complex data including computing network host device information, device light application information, and business light application information are downloaded to the local storage through the Android or iOS platform in the form of bundle package data; In step S2, a corresponding data model is designed according to the type of data, and an algorithm is used to automatically classify and preprocess the data to improve processing efficiency and accuracy; Design a single data model for single entity data including user information; For the computing network host device information, device light application information, and business light application information, tables or Realm models are designed respectively.

3. The multi-type data offline autonomous method for a computing network host management platform according to claim 1 is characterized in that: In the step S1, before the data is stored locally, the collected data is encrypted using an encryption algorithm to ensure the security of the data; Use the AES symmetric encryption algorithm and use the corresponding library to implement encryption and decryption functions.

4. The multi-type data offline autonomous method for a computing network host management platform according to claim 3 is characterized by: In step S4, based on the Android and Ios platforms, corresponding network monitoring means are used to monitor the network status in real time, so that data can be automatically uploaded when the network is restored; At the same time, an offline data queue is designed. Whenever there is data to be uploaded but the network fails, the corresponding data is added to the offline data queue; Specifically, a table is created in the local database, which contains data content, timestamp and status fields for storing data records to be uploaded; the status field includes "not uploaded" and "uploaded"; When it is detected in real time that the network connection is restored, the data to be uploaded is read from the offline data queue and uploaded to the server one by one or in batches; After the upload is successful, the status of the data record is updated to uploaded, and the corresponding data is removed from the offline data queue.

5. The multi-type data offline autonomous method for a computing network host management platform according to claim 4 is characterized in that: In step S4, when uploading data, an identity authentication mechanism based on a token Token is used for authentication; After the user logs in successfully, the server returns a token, and the client attaches the token to the HTTP request header every time it uploads data; Use Redux for state management and redux-offline middleware to handle offline data; The redux-offline middleware monitors each action data change, serializes the data that needs to be offline to the local, and restores the data from the local to the Redux store when the page is refreshed.

6. The multi-type data offline autonomous flight for the computing network host management platform according to claim 1 is characterized by: In step S4, when data is updated or synchronized, the corresponding library is used to unify the data structure and flatten the nested data, so that the data structure is clearer and easier to manage, which facilitates the storage, update and transmission of data.

7. A multi-type data offline autonomous system for a computing network host management platform, characterized by: include: The data collection module is responsible for automatically identifying and collecting various types of data in the computing network host management platform, including user information, computing network host device information, device light application information, business light application information and user behavior data, and storing the collected information locally; At the same time, the real-time nature of the data is analyzed to distinguish between real-time data and offline processed data, and only offline processed data is included in subsequent steps; The data classification and preprocessing module is responsible for classifying and processing various types of collected data, and performing necessary preprocessing operations based on data characteristics, including data cleaning, deduplication and format conversion, to ensure data accuracy and availability; The offline autonomous strategy formulation module is responsible for formulating corresponding offline autonomous strategies for each type of data based on preset autonomous rules or intelligent algorithms obtained based on historical data analysis, including data storage strategies, analysis algorithm selection, and execution plans; The offline processing execution module is responsible for executing data processing tasks outside the computing network host management platform or in a designated offline environment in accordance with the established offline autonomous strategy, thus realizing autonomous management and analysis of data. The result feedback and optimization module is responsible for feeding back the offline processing results to the computing network host management platform, and optimizing and adjusting the offline autonomous strategy according to the processing effect to improve the efficiency and accuracy of data processing.

8. The multi-type data offline autonomous system for a computing network host management platform according to claim 7 is characterized in that: It also includes a monitoring and alarm module, which is used to monitor the execution status of offline processing tasks in real time and automatically trigger the alarm mechanism when an abnormality is found.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 6.

10. A computing device, characterized in that: include: One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods according to claims 1 to 6.

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