Intelligent supervision method and system for education cloud platform

Abstract

The invention provides an intelligent supervision method and system for an education cloud platform, belongs to the technical field of education cloud platforms, and is used for solving the problem that functional requirements cannot be flexibly met in time due to the fact that resource use and allocation strategies of the education cloud platform are difficult to reasonably determine in related technologies. According to the technical scheme, the function demand of each function of the education cloud platform can be predicted, and the configuration strategy of the corresponding function module set is reconfigured according to the function demand, so that the function demand can be ensured to be satisfied by changing the resource use and distribution condition of the education cloud platform, and the overall informatization solution of the education cloud platform can be ensured to be reliably realized.

Description

Technical Field

[0001] The present application relates to the technical field of educational cloud platforms, and in particular to an intelligent supervision method and system for educational cloud platforms. Background Art

[0002] The education cloud platform is an educational 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 is used to support a specific functional requirement. One of the purposes of intelligent supervision of the education cloud platform is to optimize the resource usage and allocation of the education cloud platform, thereby ensuring that the functional requirements of the education cloud platform can be met. Summary of the invention

[0003] The present application provides an intelligent supervision method and system for an education cloud platform, which is conducive to optimizing the resource usage and allocation of the education cloud platform, and further conducive to ensuring that the functional requirements of the education cloud platform are met.

[0004] In a first aspect, the present application provides an intelligent supervision method for an education cloud platform. The education cloud platform includes a plurality of function module sets, each of which has a plurality of function modules with the same functions;

[0005] The method comprises:

[0006] Acquire operation record data of each functional module within a first preset time period, wherein the operation record data includes an operation time record and an operation content record;

[0007] Analyzing the function requirement data of each function module set within a second preset time after the current moment according to the operation record data, wherein the second preset time is shorter than the first preset time;

[0008] Configuration strategy information is determined according to the functional requirement data, and the configuration strategy information includes an adjustment amount of the number of functional modules in each functional module set.

[0009] By adopting the above technical solution, it is possible to predict the functional requirements of each function of the education cloud platform, and adapt the configuration strategy of the corresponding functional module set according to the functional requirements, so as to ensure that the functional requirements can be met by changing the resource usage and allocation of the education cloud platform, thereby ensuring that the overall information solution of the education cloud platform can be reliably implemented.

[0010] Further, analyzing the function requirement data of each function module set within a second preset time period after the current moment according to the operation record data includes:

[0011] Determine an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated to the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record;

[0012] For each functional module set, the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit time lengths before the current moment is determined as a first operation sum value, and the average value of the first operation sum values ​​of a second preset number of first unit time lengths before the current moment is calculated as the functional requirement base, and the first requirement coefficient is calculated according to the first preset number of first operation sum values, and the second preset number is less than the first preset number;

[0013] Determine a sum of a third preset number of operation quantization values ​​of operation record data within a first unit time length with a specified time as an end time as a second operation sum value, and determine a second demand coefficient according to the second operation sum value, wherein the specified time and the current time are periodic moments under a preset periodic rule;

[0014] Determine the functional requirement data according to the functional requirement base, the first requirement coefficient and the second requirement coefficient;

[0015] The first demand coefficient is obtained by substituting a first preset number of first running and values ​​into a pre-built coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second running and values ​​into the coefficient calculation model. The coefficient calculation model is used to input multiple running and values ​​carrying time identifiers to output demand coefficient result values.

[0016] Furthermore, the coefficient calculation model includes:

[0017] Suppose there are n running sums to be substituted, and the i-th running sum is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k;

[0018] According to the n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ;

[0019] Determine whether the running change values ​​are all greater than zero or are all not greater than zero;

[0020] If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

[0021] Further, the determining of configuration strategy information according to the functional requirement data includes:

[0022] Determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module;

[0023] For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data;

[0024] If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data;

[0025] If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged;

[0026] If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

[0027] In a second aspect, the present application provides an intelligent supervision system for an education cloud platform. The education cloud platform includes a plurality of function module sets, each of which has a plurality of function modules with the same functions;

[0028] The system comprises:

[0029] A data acquisition module, used to acquire the operation record data of each functional module within a first preset time period, wherein the operation record data includes an operation time record and an operation content record;

[0030] a demand analysis module, configured to analyze, according to the operation record data, the function demand data of each function module set within a second preset time period after the current moment, wherein the second preset time period is shorter than the first preset time period; and

[0031] The strategy determination module is used to determine configuration strategy information according to the functional requirement data, wherein the configuration strategy information includes an adjustment amount of the number of functional modules in each functional module set.

[0032] Furthermore, the demand analysis module is further configured to:

[0033] Determine an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated to the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record;

[0034] For each functional module set, the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit time lengths before the current moment is determined as a first operation sum value, and the average value of the first operation sum values ​​of a second preset number of first unit time lengths before the current moment is calculated as the functional requirement base, and the first requirement coefficient is calculated according to the first preset number of first operation sum values, and the second preset number is less than the first preset number;

[0035] Determine a sum of a third preset number of operation quantization values ​​of operation record data within a first unit time length with a specified time as an end time as a second operation sum value, and determine a second demand coefficient according to the second operation sum value, wherein the specified time and the current time are periodic moments under a preset periodic rule;

[0036] Determine the functional requirement data according to the functional requirement base, the first requirement coefficient and the second requirement coefficient;

[0037] The first demand coefficient is obtained by substituting a first preset number of first running and values ​​into a pre-built coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second running and values ​​into the coefficient calculation model. The coefficient calculation model is used to input multiple running and values ​​carrying time identifiers to output demand coefficient result values.

[0038] Furthermore, the demand analysis module is further configured such that the coefficient calculation model includes:

[0039] Suppose there are n running sums to be substituted, and the i-th running sum is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k;

[0040] According to the n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ;

[0041] Determine whether the running change values ​​are all greater than zero or are all not greater than zero;

[0042] If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

[0043] Furthermore, the strategy determination module is further configured to:

[0044] Determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module;

[0045] For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data;

[0046] If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data;

[0047] If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged;

[0048] If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

[0049] In summary, this application at least has the following beneficial effects:

[0050] Provided is an intelligent supervision method and system for an education cloud platform, which can change the resource usage and allocation of the education cloud platform according to predicted functional requirements, so as to ensure that the functional requirements of the education cloud platform are met.

[0051] 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

[0052] 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:

[0053] Figure 1 A flow chart showing an intelligent supervision method of an education cloud platform in an embodiment of the present application is shown;

[0054] Figure 2 A block diagram of an intelligent supervision system for an education cloud platform in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0055] 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.

[0056] 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.

[0057] The present application provides an intelligent supervision method and system for an education cloud platform, which can change the resource usage and allocation of the education cloud platform according to predicted functional requirements, so as to ensure that the functional requirements of the education cloud platform are met.

[0058] In a first aspect, the present application provides an intelligent supervision method for an education cloud platform. The method can be executed by a server, and the method is applied to an education cloud platform, wherein the education cloud platform includes multiple function module sets, each function module set having multiple modules with the same function.

[0059] Specifically, the education cloud platform has multiple functions, such as resource sharing, online teaching, learning management, interactive communication, personalized learning, intelligent analysis, home-school communication, administrative management, etc. All functions cooperate to realize the overall information solution. In the education cloud platform, each function requires multiple identical functional modules (or so-called mirrored functional modules) to meet, so that when each functional module set cannot meet the functional requirements, the education cloud platform can quickly construct a mirrored functional module based on its powerful computing power to support the realization of this part of the functional requirements and ensure the continuous reliability of the overall information solution. The method provided in the first aspect of this application is aimed at the problem in this specific scenario. A functional module set is oriented to a functional requirement and contains multiple identical functional modules.

[0060] Figure 1 A flow chart of an intelligent supervision method for an education cloud platform in an embodiment of the present application is shown.

[0061] Reference Figure 1 , the method specifically comprises the following steps:

[0062] S110: Obtaining operation record data of each functional module within a first preset time period.

[0063] The operation record data includes operation time record and operation content record.

[0064] Specifically, the operation record data reflects the operation status of the functional module, and each operation is used to execute a functional content supported by the functional module. The operation time record in the operation record data reflects the start time and end time (or initial time and final time) of the operation of the functional module. Of course, the operation duration can also be calculated to determine the operation time. The operation content record in the operation record data contains specific functional content. It should be understood that the full set of functional content that can be supported by the functional modules in each functional module set is known, and the functional content reflected by the operation content data in each operation record data is one of the corresponding functional content sets.

[0065] S120: Analyze the function requirement data of each function module set within a second preset time period after the current moment according to the operation record data.

[0066] In the method of this step, the second preset time length is shorter than the first preset time length; generally speaking, the first preset time length is ten times longer than the second preset time length.

[0067] The method of this step specifically includes: determining an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated with the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record; for each functional module set, determining the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit durations before the current moment as a first operation sum value, calculating the average value of the first operation sum values ​​within a second preset number of first unit durations before the current moment as a functional requirement base, and calculating the first requirement coefficient based on the first preset number of first operation sum values, wherein the second preset number is less than the first preset number; determining a third preset number of first operation sum values ​​with a specified value as the function requirement base; The sum of the operation quantization values ​​of the operation record data within the first unit time length with the last moment as the moment is the second operation sum value, and the second demand coefficient is determined according to the second operation sum value, and the specified moment and the current moment are periodic moments under the preset periodic rule; the functional requirement data is determined according to the functional requirement base, the first demand coefficient and the second demand coefficient; the first demand coefficient is obtained by substituting a first preset number of first operation sum values ​​into a pre-constructed coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second operation sum values ​​into the coefficient calculation model, and the coefficient calculation model is used to input multiple operation sum values ​​carrying time identifiers to output demand coefficient result values.

[0068] For the determination of the operation quantization value, specifically, a content coefficient comparison table can be pre-built for each functional module set. The content coefficient comparison table contains the comparison relationship between all operation content records and operation coefficients, that is, for each operation content record, an operation coefficient can be obtained by substituting it into the content coefficient comparison table. Generally speaking, the greater the consumption or the more computing power the operation content record brings to the functional module, the greater the operation coefficient. The operation duration itself is quantitative data. In this way, the operation quantization value can be determined by combining the operation duration and the operation coefficient, and the operation quantization value is positively correlated with the operation duration and the operation coefficient.

[0069] In one specific example, the run quantization value is equal to the product of the run length and the run coefficient.

[0070] In the method of this step, the second preset number is generally 2-5, and specifically 3, and the first preset number is generally ten times greater than the second preset number.

[0071] In the method of this step, the third preset number is generally 3-10, and specifically 7. The periodic moments under the preset periodic rule may specifically refer to the same time on different days (e.g., 10:15), the same time on the same day of different weeks (e.g., 10:15 on Wednesday), or the same time on the same day of different months (e.g., 10:15 on the 2nd), etc. The preset periodic rule can be pre-constructed by itself. After the current moment is determined, the specified moment can be determined based on the current moment and the preset periodic rule. In an embodiment of the present application, the third preset number of specified moments are closest to the current moment and are continuous.

[0072] The first demand coefficient and the second demand coefficient are specifically calculated using a pre-constructed coefficient calculation model. Specifically, the coefficient calculation model is specifically introduced below.

[0073] In one example, the coefficient calculation model includes: assuming that there are n running and values ​​to be substituted, and the i-th running and value is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k;

[0074] According to the n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ;

[0075] Determine whether the running change values ​​are all greater than zero or are all not greater than zero;

[0076] If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

[0077] In this way, the first demand coefficient can be calculated based on the first preset number of first running sum values, and the second demand coefficient can be calculated based on the second preset number of second running sum values.

[0078] By combining the methods of this step, the functional requirement data of each functional module set can be finally obtained. The larger the functional requirement data, the more functional modules are needed to support its implementation.

[0079] S130: Determine configuration strategy information according to the functional requirement data, wherein the configuration strategy information includes an adjustment amount of the number of functional modules in each functional module set.

[0080] The method of this step includes: determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module;

[0081] For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data;

[0082] If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data;

[0083] If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged;

[0084] If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

[0085] The unit function data reflects the functional requirements that each function module in the function module set can meet. After determining the configuration strategy information of each function module set, the configuration strategy of the function module set can be adjusted according to the configuration strategy information, such as increasing, reducing or keeping the number of function modules unchanged. That is, by optimizing the use of resources and configuration strategies, it is ensured that the functional requirement data of the function module set in a period of time can be reliably met, thereby ensuring that the overall information solution of the education cloud platform can be reliably implemented.

[0086] 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.

[0087] In a second aspect, the present application provides an intelligent supervision system for an education cloud platform. The system may be included in a server.

[0088] Figure 2 A block diagram of an intelligent supervision system for an education cloud platform in an embodiment of the present application is shown.

[0089] Reference Figure 2 , the educational cloud platform includes several functional module sets, each functional module set has multiple functional modules with the same functions;

[0090] The system comprises:

[0091] A data acquisition module 210 is used to acquire the operation record data of each functional module within a first preset time period, wherein the operation record data includes an operation time record and an operation content record;

[0092] A demand analysis module 220, configured to analyze the functional demand data of each functional module set within a second preset time period after the current moment according to the operation record data, wherein the second preset time period is shorter than the first preset time period; and

[0093] The strategy determination module 230 is used to determine configuration strategy information according to the functional requirement data, wherein the configuration strategy information includes an adjustment amount of the number of functional modules in each functional module set.

[0094] Furthermore, the demand analysis module 220 is further configured to:

[0095] Determine an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated to the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record;

[0096] For each functional module set, the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit time lengths before the current moment is determined as a first operation sum value, and the average value of the first operation sum values ​​of a second preset number of first unit time lengths before the current moment is calculated as the functional requirement base, and the first requirement coefficient is calculated according to the first preset number of first operation sum values, and the second preset number is less than the first preset number;

[0097] Determine a sum of a third preset number of operation quantization values ​​of operation record data within a first unit time length with a specified time as an end time as a second operation sum value, and determine a second demand coefficient according to the second operation sum value, wherein the specified time and the current time are periodic moments under a preset periodic rule;

[0098] Determine the functional requirement data according to the functional requirement base, the first requirement coefficient and the second requirement coefficient;

[0099] The first demand coefficient is obtained by substituting a first preset number of first running and values ​​into a pre-built coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second running and values ​​into the coefficient calculation model. The coefficient calculation model is used to input multiple running and values ​​carrying time identifiers to output demand coefficient result values.

[0100] Furthermore, the demand analysis module 220 is further configured such that the coefficient calculation model includes:

[0101] Suppose there are n running sums to be substituted, and the i-th running sum is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k;

[0102] According to the n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ;

[0103] Determine whether the running change values ​​are all greater than zero or are all not greater than zero;

[0104] If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

[0105] Furthermore, the strategy determination module 230 is further configured to:

[0106] Determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module;

[0107] For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data;

[0108] If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data;

[0109] If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged;

[0110] If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0112] In summary, this application at least has the following beneficial effects:

[0113] Provided is an intelligent supervision method and system for an education cloud platform, which can change the resource usage and allocation of the education cloud platform according to predicted functional requirements, so as to ensure that the functional requirements of the education cloud platform are met.

[0114] 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. An intelligent supervision method for an education cloud platform, characterized in that: The educational cloud platform includes several functional module sets, each of which has multiple functional modules with the same functions; The method comprises: Acquire operation record data of each functional module within a first preset time period, wherein the operation record data includes an operation time record and an operation content record; Analyzing the function requirement data of each function module set within a second preset time after the current moment according to the operation record data, wherein the second preset time is shorter than the first preset time; Configuration strategy information is determined according to the functional requirement data, and the configuration strategy information includes an adjustment amount of the number of functional modules in each functional module set.

2. According to claim 1, the intelligent supervision method of an education cloud platform is characterized in that: The analyzing the function requirement data of each function module set within a second preset time period after the current moment according to the operation record data includes: Determine an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated to the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record; For each functional module set, the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit time lengths before the current moment is determined as a first operation sum value, and the average value of the first operation sum values ​​of a second preset number of first unit time lengths before the current moment is calculated as the functional requirement base, and the first requirement coefficient is calculated according to the first preset number of first operation sum values, and the second preset number is less than the first preset number; Determine a sum of a third preset number of operation quantization values ​​of operation record data within a first unit time length with a specified time as an end time as a second operation sum value, and determine a second demand coefficient according to the second operation sum value, wherein the specified time and the current time are periodic moments under a preset periodic rule; Determine the functional requirement data according to the functional requirement base, the first requirement coefficient and the second requirement coefficient; The first demand coefficient is obtained by substituting a first preset number of first running and values ​​into a pre-built coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second running and values ​​into the coefficient calculation model. The coefficient calculation model is used to input multiple running and values ​​carrying time identifiers to output demand coefficient result values.

3. The intelligent supervision method of an education cloud platform according to claim 2 is characterized in that: The coefficient calculation model includes: Suppose there are n running sums to be substituted, and the i-th running sum is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k; According to n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ; Determine whether the running change values ​​are all greater than zero or are all not greater than zero; If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

4. The intelligent supervision method of an education cloud platform according to claim 3 is characterized in that: Determining the configuration strategy information according to the functional requirement data includes: Determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module; For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data; If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data; If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged; If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

5. An intelligent supervision system for an education cloud platform, characterized in that: The educational cloud platform includes several functional module sets, each of which has multiple functional modules with the same functions; The system comprises: A data acquisition module (210) is used to acquire operation record data of each functional module within a first preset time period, wherein the operation record data includes an operation time record and an operation content record; A demand analysis module (220), configured to analyze the functional demand data of each functional module set within a second preset time period after the current moment according to the operation record data, wherein the second preset time period is shorter than the first preset time period; and A strategy determination module (230) is used to determine configuration strategy information according to the functional requirement data, wherein the configuration strategy information includes an adjustment amount for the number of functional modules in each functional module set.

6. The intelligent supervision system of an education cloud platform according to claim 5, characterized in that: The demand analysis module (220) is further configured to: Determine an operation quantization value for each operation record data, wherein the operation quantization value is positively correlated to the operation duration reflected by the operation time record and the operation coefficient preset in the corresponding operation content record; For each functional module set, the sum of the operation quantization values ​​of the operation record data within a first preset number of first unit time lengths before the current moment is determined as a first operation sum value, and the average value of the first operation sum values ​​of a second preset number of first unit time lengths before the current moment is calculated as the functional requirement base, and the first requirement coefficient is calculated according to the first preset number of first operation sum values, and the second preset number is less than the first preset number; Determine a sum of a third preset number of operation quantization values ​​of operation record data within a first unit time length with a specified time as an end time as a second operation sum value, and determine a second demand coefficient according to the second operation sum value, wherein the specified time and the current time are periodic moments under a preset periodic rule; Determine the functional requirement data according to the functional requirement base, the first requirement coefficient and the second requirement coefficient; The first demand coefficient is obtained by substituting a first preset number of first running and values ​​into a pre-built coefficient calculation model, and the second demand coefficient is obtained by substituting a second preset number of second running and values ​​into the coefficient calculation model. The coefficient calculation model is used to input multiple running and values ​​carrying time identifiers to output demand coefficient result values.

7. The intelligent supervision system of an education cloud platform according to claim 6, characterized in that: The demand analysis module (220) is further configured such that the coefficient calculation model includes: Suppose there are n running sums to be substituted, and the i-th running sum is a i , a1, a2, ..., a n The time tags carried are from near to far from the current time, and the coefficient result value is k; According to n running and values, n-1 running change values ​​are calculated, and the i-th running change value is b i , then b i =a i -a i+1 ; Determine whether the running change values ​​are all greater than zero or are all not greater than zero; If so, then otherwise, Wherein, k is the coefficient result value, p is the number of running change values ​​greater than zero, q is the number of running change values ​​not greater than zero, and δ1 and δ2 are both preset constants greater than 0 and less than 1.

8. The intelligent supervision system of an educational cloud platform according to claim 7, characterized in that: The policy determination module (230) is further configured to: Determining functional quantity data of each functional module set based on pre-acquired module functional data of each functional module; For each functional module set, the function difference data is calculated by subtracting the function requirement data from the function quantity data; If the difference value of the function modules of the function module set is greater than the corresponding preset unit function data, the configuration strategy information of the function module set is to reduce the number of modules, and the number of reduced modules is the rounded-down value of the result obtained by dividing the function difference data by the corresponding unit function data; If the difference value of the function modules of the function module set is not less than zero and not greater than the corresponding preset unit function data, the configuration strategy information of the function module set is that the number of modules remains unchanged; If the function module difference of the function module set is less than zero, the configuration strategy information of the function module set is to increase the number of modules, and the number of increased modules is the rounded-up value of the result obtained by dividing the absolute value of the function module difference by the corresponding unit function data.

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