Data management method and system based on education cloud platform

Abstract

The invention provides a data management method and system based on an education cloud platform, belongs to the technical field of education cloud platforms, and is used for solving the problem that the reliability of data of the education cloud platform is difficult to effectively guarantee in related technologies. Obtaining operation record data of each function module and a data historical calling record of a corresponding function database within a first preset duration; determining storage reliability data of a function database according to the operation record data and the data historical calling record; when the storage reliability data is lower than the reliability threshold value, storage state abnormity prompt information of the function database is generated, so that the storage reliability of each function database of the education cloud platform can be guaranteed, namely, the reliability of data related to the education cloud platform can be guaranteed; and therefore, reliable data support can be ensured to exist in function realization of each function module of the education cloud platform, and reliable function realization of the education cloud platform can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of educational cloud platforms, and in particular to a data management method and system based on educational cloud platforms. Background Art

[0002] The education cloud platform is an education information solution based on cloud computing technology. It integrates teaching, management, learning, and resource sharing functions into a unified online platform. The core advantages of the education cloud platform lie in its flexibility, scalability, and efficient use of resources. The education cloud platform generally includes multiple functions such as resource sharing, online teaching, learning management, interactive communication, personalized learning, intelligent analysis, home-school communication, and administrative management. Each function requires the support of corresponding data during its implementation and will generate corresponding usage data. The reliability of the functions of the education cloud platform requires the reliability of the corresponding data to support it. Summary of the invention

[0003] The present application provides a data management method and system based on an education cloud platform, which is conducive to ensuring the reliability of data related to the education cloud platform.

[0004] In a first aspect, the present application provides a data management method based on an education cloud platform. The education cloud platform includes a plurality of functional modules, and each functional module is configured with a functional database;

[0005] The method comprises:

[0006] Obtaining the operation record data of each functional module and the data history call record of the corresponding functional database within a first preset time period;

[0007] Determine storage reliability data of a functional database according to the operation record data and the data history call record;

[0008] When the storage reliability data is lower than the reliability threshold, a storage status abnormality prompt message of the functional database is generated.

[0009] By adopting the above technical solution, it is helpful to ensure the storage reliability of each functional database of the education cloud platform, that is, it is helpful to ensure the reliability of data related to the education cloud platform, and further to ensure that there is reliable data support for the functional implementation of each functional module of the education cloud platform, which is conducive to ensuring the reliable implementation of the functions of the education cloud platform.

[0010] Furthermore, the operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is a normal call or an abnormal call;

[0011] The storage reliability data of the function database determined according to the operation record data and the data history call record includes:

[0012] Calculate the abnormality influence coefficient according to the operation record data and the historical call record;

[0013] The storage reliability data is determined according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

[0014] Further, the calculating of the abnormality influence coefficient according to the operation record data and the historical call record includes:

[0015] Dividing the first preset duration into a preset number of second preset durations;

[0016] Determining a running abnormality rate and a calling abnormality rate relative to each second preset time length;

[0017] Calculate storage abnormality data according to the call abnormality rate and the operation abnormality rate, wherein the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate;

[0018] Calculating the abnormality change rate of each two adjacent second preset time periods according to the stored abnormality data;

[0019] Determine whether the abnormality change rates are all greater than zero or not greater than zero;

[0020] If yes, then k = 1-(k1+v1), if no, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and v2 is the abnormality change rate between the two preset time lengths closest to the current moment.

[0021] Further, the calculating and storing abnormality degree data according to the call abnormality rate and the operation abnormality rate includes:

[0022] For each second preset time length, assuming that the operation abnormality rate is a, the call abnormality rate is b, and the storage abnormality data is z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero.

[0023] Further, the determining of the storage reliability data according to the abnormality influence coefficient and the pre-acquired storage reliability base value includes: w0=w pre ×k, where w0 is the base value of storage reliability, w pre To store reliability data.

[0024] In a second aspect, the present application provides a data management system based on an education cloud platform. The education cloud platform includes a plurality of functional modules, and each functional module is configured with a functional database;

[0025] The system comprises:

[0026] A data acquisition module, used to acquire the operation record data of each functional module and the data history call record of the corresponding functional database within a first preset time period;

[0027] a reliability determination module, configured to determine storage reliability data of a function database according to the operation record data and the data history call record; and

[0028] The prompt generation module is used to generate storage status abnormal prompt information of the functional database when the storage reliability data is lower than the reliability threshold.

[0029] Further, in the data acquisition module, the operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is a normal call or an abnormal call;

[0030] The reliability determination module is further configured to:

[0031] Calculate the abnormality influence coefficient according to the operation record data and the historical call record;

[0032] The storage reliability data is determined according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

[0033] Furthermore, the reliability determination module is further configured to:

[0034] Dividing the first preset duration into a preset number of second preset durations;

[0035] Determining a running abnormality rate and a calling abnormality rate relative to each second preset time length;

[0036] Calculate storage abnormality data according to the call abnormality rate and the operation abnormality rate, wherein the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate;

[0037] Calculating the abnormality change rate of each two adjacent second preset time periods according to the stored abnormality data;

[0038] Determine whether the abnormality change rates are all greater than zero or not greater than zero;

[0039] If yes, then k = 1-(k1+v1), if no, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and v2 is the abnormality change rate between the two preset time lengths closest to the current moment.

[0040] Furthermore, the reliability determination module is further configured to:

[0041] For each second preset time length, assuming that the operation abnormality rate is a, the call abnormality rate is b, and the storage abnormality data is z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero.

[0042] Furthermore, the reliability determination module is further configured as follows: w0=w pre ×k, where w0 is the base value of storage reliability, w pre To store reliability data.

[0043] In summary, this application at least has the following beneficial effects:

[0044] Provided are a data management method and system based on an education cloud platform, which are beneficial to ensuring the reliability of data related to the education cloud platform.

[0045] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0047] Figure 1 A flow chart of a data management method based on an education cloud platform in an embodiment of the present application is shown;

[0048] Figure 2 A block diagram of a data management system based on an education cloud platform in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0051] The present application provides a data management method and system based on an education cloud platform, which is conducive to ensuring the reliability of data related to the education cloud platform.

[0052] In a first aspect, the present application provides a data management method based on an education cloud platform. The method can be executed by a server. The method is applied to an education cloud platform, and the education cloud platform includes multiple functional modules.

[0053] Specifically, the education cloud platform has a variety of functions, such as resource sharing, online teaching, learning management, interactive communication, personalized learning, intelligent analysis, home-school communication, administrative management, etc. All functions work together to achieve an overall information solution. In the education cloud platform, each function is implemented using a functional module, and a functional database is pre-configured relative to each functional module. The functional database is used to store data related to the functional module to support the functional implementation of the corresponding functional module. The functional implementation of the education cloud platform requires not only the reliable operation of the functional module, but also the accuracy of the data in the functional database. When an abnormal failure occurs in the functional database, data errors or losses may occur, making it difficult to reliably implement the functions of the functional module. The method provided in the first aspect of this application is aimed at the problem in this specific scenario, that is, how to ensure the reliability of the corresponding data of the functional module to ensure the reliable implementation of the functions of the functional module.

[0054] Figure 1 A flow chart of a data management method based on an education cloud platform in an embodiment of the present application is shown.

[0055] Reference Figure 1 , the method specifically comprises the following steps:

[0056] S110: Acquire the operation record data of each functional module within a first preset time period and the data history call record of the corresponding functional database.

[0057] In the method of this step, the operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is a normal call or an abnormal call.

[0058] S120: Determine storage reliability data of the function database according to the operation record data and the data history call record.

[0059] The method of this step specifically includes: calculating an abnormality influence coefficient according to the operation record data and the historical call record; and determining the storage reliability data according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

[0060] Specifically, the calculation of the abnormality influence coefficient according to the operation record data and the historical call record includes: dividing the first preset time length into a preset number of second preset time lengths; determining an operation abnormality rate and a call abnormality rate relative to each second preset time length; calculating a storage abnormality data according to the call abnormality rate and the operation abnormality rate, and the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate; calculating the abnormality change rate of each two adjacent second preset time lengths according to the storage abnormality data; judging whether the abnormality change rates are all greater than zero or not greater than zero; if so, then k=1-(k1+v1), if not, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and v2 is the abnormality change rate between the two preset time lengths closest to the current moment.

[0061] The calculation of the storage abnormality data according to the call abnormality rate and the operation abnormality rate includes: for each second preset time length, the operation abnormality rate is set to a, the call abnormality rate is set to b, and the storage abnormality data is set to z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero. In the embodiment of the present application, the sum of K1 and K2 is equal to 1 and K1 is greater than K2, and K1 can be specifically taken as 0.7 and K2 as 0.3.

[0062] The determining of the storage reliability data according to the abnormality influence coefficient and the pre-acquired storage reliability base value includes: w0=w pre ×k, where w0 is the base value of storage reliability, w pre In the embodiment of the present application, the storage reliability base value is preset relative to each function database and is a constant greater than zero, which is positively correlated to the performance parameters of the function database and negatively correlated to the difficulty of data call of the function database.

[0063] S130: When the storage reliability data is lower than the reliability threshold, generating storage status abnormality prompt information of the function database.

[0064] If the storage reliability data is lower than the reliability threshold, it means that the abnormality of the function database is high and the function database needs to be repaired and maintained. The storage status abnormality prompt information shows this state of the function database. When the storage status abnormality prompt information for the function database appears, the data in the function database can be backed up to the backup database, and the function database can be repaired and maintained to avoid data loss in the function database. When the repair and maintenance of the function database is completed, the corresponding data can be stored in the function database.

[0065] This method can be executed periodically or manually triggered.

[0066] In summary, the method can timely discover storage anomalies of the functional database and respond to the abnormal state of the functional database in a timely manner, so as to ensure the reliability of the data of the functional module, and further help to ensure the reliable implementation of the functions of the functional module.

[0067] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0068] In a second aspect, the present application provides a data management system based on an education cloud platform. The system may be included in a server. The education cloud platform includes a plurality of functional modules, and a functional database is configured for each functional module.

[0069] Figure 2 A block diagram of a data management system based on an education cloud platform in an embodiment of the present application is shown.

[0070] Reference Figure 2 , the system comprising:

[0071] The data acquisition module 210 is used to acquire the operation record data of each functional module and the data history call record of the corresponding functional database within a first preset time period;

[0072] A reliability determination module 220, configured to determine storage reliability data of a function database according to the operation record data and the data history call record; and

[0073] The prompt generation module 230 is used to generate storage status abnormality prompt information of the function database when the storage reliability data is lower than the reliability threshold.

[0074] Further, in the data acquisition module 210, the operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is a normal call or an abnormal call;

[0075] The reliability determination module 220 is further configured to:

[0076] Calculate the abnormality influence coefficient according to the operation record data and the historical call record;

[0077] The storage reliability data is determined according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

[0078] Furthermore, the reliability determination module 220 is further configured to:

[0079] Dividing the first preset duration into a preset number of second preset durations;

[0080] Determining a running abnormality rate and a calling abnormality rate relative to each second preset time length;

[0081] Calculate storage abnormality data according to the call abnormality rate and the operation abnormality rate, wherein the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate;

[0082] Calculating the abnormality change rate of each two adjacent second preset time periods according to the stored abnormality data;

[0083] Determine whether the abnormality change rates are all greater than zero or not greater than zero;

[0084] If yes, then k = 1-(k1+v1), if no, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and v2 is the abnormality change rate between the two preset time lengths closest to the current moment.

[0085] Furthermore, the reliability determination module 220 is further configured to:

[0086] For each second preset time length, assuming that the operation abnormality rate is a, the call abnormality rate is b, and the storage abnormality data is z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero.

[0087] Furthermore, the reliability determination module 220 is further configured as follows: w0=w pre ×k, where w0 is the base value of storage reliability, w pre To store reliability data.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0089] In summary, this application at least has the following beneficial effects:

[0090] Provided are a data management method and system based on an education cloud platform, which are beneficial to ensuring the reliability of data related to the education cloud platform.

[0091] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A data management method based on an education cloud platform, characterized in that: The educational cloud platform includes a plurality of functional modules, and each functional module is configured with a functional database; The method comprises: Obtaining the operation record data of each functional module and the data history call record of the corresponding functional database within a first preset time period; Determine storage reliability data of a functional database according to the operation record data and the data history call record; When the storage reliability data is lower than the reliability threshold, a storage status abnormality prompt message of the functional database is generated.

2. According to the data management method based on the education cloud platform according to claim 1, it is characterized in that: The operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is a normal call or an abnormal call; The storage reliability data of the function database determined according to the operation record data and the data history call record includes: Calculate the abnormality influence coefficient according to the operation record data and the historical call record; The storage reliability data is determined according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

3. A data management method based on an education cloud platform according to claim 2, characterized in that: The calculation of the abnormality influence coefficient according to the operation record data and the historical call record comprises: Dividing the first preset duration into a preset number of second preset durations; Determining a running abnormality rate and a calling abnormality rate relative to each second preset time length; Calculate storage abnormality data according to the call abnormality rate and the operation abnormality rate, wherein the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate; Calculating the abnormality change rate of each two adjacent second preset time periods according to the stored abnormality data; Determine whether the abnormality change rates are all greater than zero or not greater than zero; If yes, then k = 1-(k1+V1), if no, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and V2 is the abnormality change rate between the two preset time lengths closest to the current moment.

4. A data management method based on an educational cloud platform according to claim 3, characterized in that: The calculating and storing abnormality degree data according to the call abnormality rate and the operation abnormality rate comprises: For each second preset time length, assuming that the operation abnormality rate is a, the call abnormality rate is b, and the storage abnormality data is z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero.

5. A data management method based on an educational cloud platform according to claim 4, characterized in that: The determining of the storage reliability data according to the abnormality influence coefficient and the pre-acquired storage reliability base value includes: w0=w pre ×k, where w0 is the base value of storage reliability, w pre To store reliability data.

6. A data management system based on an education cloud platform, characterized in that: The educational cloud platform includes a plurality of functional modules, and each functional module is configured with a functional database; The system comprises: A data acquisition module (210) is used to acquire the operation record data of each functional module and the data history call record of the corresponding functional database within a first preset time period; A reliability determination module (220), used to determine storage reliability data of a function database according to the operation record data and the data history call record; and The prompt generation module (230) is used to generate storage state abnormality prompt information of the function database when the storage reliability data is lower than the reliability threshold.

7. A data management system based on an educational cloud platform according to claim 5, characterized in that: In the data acquisition module (210), the operation record data and the historical call record both carry a timestamp, the operation record data also carries an operation result, the historical call record carries a call result, the operation result is normal operation or abnormal operation, and the call result is normal call or abnormal call; The reliability determination module (220) is further configured to: Calculate the abnormality influence coefficient according to the operation record data and the historical call record; The storage reliability data is determined according to the abnormality influence coefficient and a pre-acquired storage reliability base value.

8. A data management system based on an educational cloud platform according to claim 7, characterized in that: The reliability determination module (220) is further configured to: Dividing the first preset duration into a preset number of second preset durations; Determining a running abnormality rate and a calling abnormality rate relative to each second preset time length; Calculate storage abnormality data according to the call abnormality rate and the operation abnormality rate, wherein the storage abnormality data is positively correlated with the operation abnormality rate and the call abnormality rate; Calculating the abnormality change rate of each two adjacent second preset time periods according to the stored abnormality data; Determine whether the abnormality change rates are all greater than zero or not greater than zero; If yes, then k = 1-(k1+V1), if no, then Wherein, k is the abnormality influence coefficient, k1 is the stored abnormality data of the most recent second preset time length, v1 is the average value of all abnormality change rates, c is the number of abnormality change rates greater than zero, d is the number of abnormality change rates not greater than zero, and v2 is the abnormality change rate between the two preset time lengths closest to the current moment.

9. A data management system based on an educational cloud platform according to claim 8, characterized in that: The reliability determination module (220) is further configured to: For each second preset time length, assuming that the operation abnormality rate is a, the call abnormality rate is b, and the storage abnormality data is z, then z=K1a+K2b, where K1 and K2 are both preset constants greater than zero.

10. A data management system based on an educational cloud platform according to claim 9, characterized in that: The reliability determination module (220) is further configured as follows: w0=w pre ×k, where w0 is the base value of storage reliability, w pre To store reliability data.

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