College online education platform management system based on cloud big data

Abstract

The invention relates to the technical field of education platform management, in particular to a university online education platform management system based on cloud big data, which comprises an online education management center, a data acquisition unit, a page supervision unit, a content evaluation unit, an information verification unit, an optimization evaluation unit and an online management unit. According to the invention, analysis is carried out from the perspective of the optimized operation process to understand the optimization effect of the online education platform, i.e., visual operation supervision evaluation analysis is carried out from the perspective of visibility and virtual operation supervision evaluation analysis is carried out from the perspective of invisibility, so that the operation stability and rapidness of the online education platform are improved; information integrity supervision and evaluation analysis is further performed on the information display data from a visual angle in an information feedback mode so as to improve the optimization effect of the online education platform, and optimization long-term supervision and evaluation analysis is performed in an information feedback and fusion mode; and the continuity and the long-term effect of the optimization operation effect of the online education platform are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of education platform management, and in particular to an online education platform management system for colleges and universities based on cloud big data. Background Art

[0002] With the rapid development and popularization of the Internet, online education has become an emerging educational method. The online education platform is an educational application based on Internet technology. A large number of students and teachers participate in online learning and education training through the Internet. It combines Internet technology with educational means to provide convenience for students, teachers and educational institutions, and inject new vitality into the education cause. However, the traditional online education system also has some problems: it is impossible to supervise the operation of the optimized online education platform, which affects the operation stability and information integrity of the online education platform, and is not conducive to feedback management of problems after optimization, and is not conducive to adjusting optimization decisions, thereby reducing the sustainability and long-term effectiveness of the optimized operation effect of the online education platform; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a university online education platform management system based on cloud big data to solve the above-mentioned technical defects. The present invention analyzes from the perspective of the post-optimization operation process to understand the optimization effect of the online education platform, that is, initially performing visual operation supervision and evaluation analysis on the operation performance data from a visual perspective and performing virtual operation supervision and evaluation analysis on the code operation data from an invisible perspective to improve the operation stability and speed of the online education platform, and further performing information integrity supervision and evaluation analysis on the information display data from a visual perspective through information feedback to improve the optimization effect of the online education platform, and performing optimization long-term supervision and evaluation analysis through information feedback and fusion to understand the sustainability of the optimization effect corresponding to the optimization decision of the online education platform, so as to adjust the current optimization decision to improve the sustainability and long-term effectiveness of the optimization operation effect of the online education platform.

[0004] The purpose of the present invention can be achieved by the following technical solutions: A university online education platform management system based on cloud big data, including an online education management center, a data collection unit, a page supervision unit, a content evaluation unit, an information verification unit, an optimization evaluation unit and an online management unit; The data collection unit is used to collect the operation performance data and code operation data of the online education platform, and send the operation performance data and code operation data to the page supervision unit and the content evaluation unit respectively; Upon receiving the operation performance data, the page supervision unit immediately performs a visual operation supervision evaluation analysis on the operation performance data, performs a comparison process on the obtained operation evaluation coefficients, and obtains a normal signal and an alarm signal; The information verification unit is used to respond to the normal signal to collect the information display data of the online education platform, and to perform information integrity supervision, evaluation and analysis on the information display data, and to compare and process the obtained residual interference coefficient to obtain a stable signal and an interference signal; After receiving the code operation data, the content evaluation unit immediately performs virtual operation supervision evaluation analysis on the code operation data, compares the obtained code optimization evaluation coefficients, and obtains qualified signals and unqualified signals. The optimization and evaluation unit optimizes the long-term regulatory evaluation and analysis through information feedback and information fusion, and sends the obtained control signals to the online management unit.

[0005] Preferably, the visibility operation supervision evaluation and analysis process of the page supervision unit is as follows: The duration corresponding to the set operation period of the online education platform after optimization is collected and set as the time threshold, and the operation performance data of the online education platform within the time threshold is obtained, the operation performance data includes the jamming frequency and the average duration of page switching, and the jamming frequency and the average duration of page switching are compared and analyzed with the preset jamming frequency threshold and the preset average duration threshold of page switching that are internally recorded and stored. If the jamming frequency is greater than the preset jamming frequency threshold, or the average duration of page switching is greater than the preset average duration threshold of page switching, a management signal is generated, and then the frequency of generating management signals of the online education platform within the time threshold is obtained, and the frequency of generating management signals of the online education platform is set as the operation evaluation coefficient; Compare and analyze the operation evaluation coefficient with the preset operation evaluation coefficient range stored internally: If the operation evaluation coefficient is greater than the maximum value of the ratio between the preset operation evaluation coefficient intervals, a normal signal is generated; If the ratio between the operation evaluation coefficient and the preset operation evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated.

[0006] Preferably, the information integrity supervision assessment and analysis process of the information verification unit is as follows: The time threshold is divided into i sub-time periods, where i is a natural number greater than zero, and the information display data of the online education platform in each sub-time period is obtained, the information display data including the defect interference value and the reading influence value, and the defect interference value and the reading influence value are compared and analyzed with the stored preset defect interference value threshold and the preset reading influence value threshold. If the defect interference value is greater than or equal to the preset defect interference value threshold, or the reading influence value is greater than or equal to the preset reading influence value threshold, the corresponding sub-time period is set as the optimized legacy interference time period, the ratio between the number of optimized legacy interference time periods and the total number of sub-time periods is obtained, and it is set as the legacy interference coefficient, and the legacy interference coefficient is judged and processed: If the legacy interference coefficient is less than a preset legacy interference coefficient threshold, a stable signal is generated; If the legacy interference coefficient is greater than or equal to a preset legacy interference coefficient threshold, an interference signal is generated.

[0007] Preferably, the defect interference value indicates the duration of information defects on the page in the page feature video of the online education platform within the sub-time period; the reading impact value indicates the duration of overlap of jitter and ripples on the page in the page feature video of the online education platform within the sub-time period.

[0008] Preferably, the virtual operation supervision evaluation and analysis process of the content evaluation unit is as follows: The code running data of the online education platform within the time threshold is obtained. The code running data represents the code running speed curve. The code optimization evaluation coefficient is obtained by analyzing the curve information of the code running speed curve, and the code optimization evaluation coefficient is compared and analyzed with the preset code optimization evaluation coefficient threshold stored in the internal storage: If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is less than 1, a qualified signal is generated; If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is greater than or equal to 1, a failure signal is generated.

[0009] Preferably, the curve information analysis process of the code running speed curve is as follows: The code running speed curve is collected once every t1 time period to obtain the code running speed, and a set A1 of code running speeds is constructed. The code running speed curve is collected once every t2 time period to obtain the code running speed, and a set A2 of code running speeds is constructed. The code running speed curve is collected once every tg time period to obtain the code running speed, and a set Ag of code running speeds is constructed. t1, t2...tg are not equal, g is a natural number greater than zero, and then the means corresponding to the sets A1, A2...Ag are obtained, and the difference between the maximum value and the minimum value in the means corresponding to the sets A1, A2...Ag is further obtained, and it is set as the code optimization evaluation coefficient.

[0010] Preferably, the optimization long-term regulatory evaluation analysis process of the optimization evaluation unit is as follows: The operation evaluation coefficient corresponding to the normal signal of the online education platform within the time threshold is obtained, and the code optimization evaluation coefficient corresponding to the qualified signal of the online education platform within the time threshold is obtained, and the legacy interference coefficient corresponding to the stable signal of the online education platform within the time threshold is obtained. The operation evaluation coefficient, the code optimization evaluation coefficient and the legacy interference coefficient are labeled as YP, DY and YL respectively, and the operation evaluation coefficient YP, the code optimization evaluation coefficient DY and the legacy interference coefficient YL are substituted into the formula Obtaining a platform optimization evaluation coefficient, wherein a1, a2, and a3 are preset proportional factor coefficients of the operation evaluation coefficient, the code optimization evaluation coefficient, and the legacy interference coefficient, respectively, a4 is a preset correction factor coefficient, a1, a2, a3, and a4 are all greater than zero, and R is the platform optimization evaluation coefficient; Then, the duration between the end of the online education platform optimization and the time when the platform optimization evaluation coefficient is equal to the preset platform optimization evaluation coefficient threshold is obtained, and it is set as the optimization efficiency continuity coefficient, and the optimization efficiency continuity coefficient is compared and analyzed with the preset optimization efficiency continuity coefficient threshold stored in the internal data: If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is greater than or equal to 1, no signal is generated; If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is less than 1, a control signal is generated.

[0011] The beneficial effects of the present invention are as follows: The present invention analyzes from the perspective of the post-optimization operation process to understand the optimization effect of the online education platform, that is, initially performs visual operation supervision and evaluation analysis on the operation performance data from a visual perspective to understand the operation status of the online education platform after optimization, so as to rationally manage the online education platform according to the feedback text, so as to improve the fluency of information reading on the online education platform and improve the user experience, and at the same time, performs virtual operation supervision and evaluation analysis on the code operation data from an invisible perspective, so as to understand the optimization status of the online education platform from the perspective of code operation, so as to improve the operation stability and speed of the online education platform; The present invention further performs information integrity supervision, evaluation and analysis on the information display data from a visual perspective through information feedback, that is, further supervises the optimization from the information display situation of the online education platform, so as to provide timely feedback on the problems existing after the optimization, so as to improve the optimization effect of the online education platform; and performs optimization long-term supervision, evaluation and analysis through information feedback and fusion, so as to understand the sustainability of the optimization effect corresponding to the optimization decision of the online education platform, so as to adjust the current optimization decision, so as to improve the sustainability and long-term effectiveness of the optimization operation effect of the online education platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings; Figure 1 It is a flowchart of the system of the present invention; Figure 2 It is a partial reference schematic diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 are within the scope of protection of the present invention. Embodiment 1

[0014] See also Figure 1 to Figure 2As shown, the present invention is an online education platform management system for colleges and universities based on cloud big data, including an online education management center, a data acquisition unit, a page supervision unit, a content evaluation unit, an information verification unit, an optimization evaluation unit and an online management unit. The online education management center is connected to the data acquisition unit in a one-way communication manner, the data acquisition unit is connected to the page supervision unit and the content evaluation unit in a one-way communication manner, the page supervision unit is connected to the information verification unit and the online management unit in a one-way communication manner, the information verification unit and the content evaluation unit are connected to the optimization evaluation unit and the online management unit in a one-way communication manner, and the optimization evaluation unit is connected to the online management unit in a one-way communication manner; When the online education management center generates a supervision instruction, the supervision instruction is sent to the data collection unit. After receiving the supervision instruction, the data collection unit immediately collects the operation performance data and code operation data of the online education platform, and sends the operation performance data and code operation data to the page supervision unit and the content evaluation unit respectively. It should be noted that the online education platform includes but is not limited to the online education platform of colleges and universities; After receiving the operation performance data, the page supervision unit immediately conducts a visual operation supervision evaluation and analysis on the operation performance data to understand the operation status of the online education platform after optimization, so as to rationally manage the online education platform according to the feedback text, so as to improve the fluency of information reading on the online education platform and improve the user experience. The specific visual operation supervision evaluation and analysis process is as follows: The duration corresponding to the set operation period of the online education platform after optimization is collected and set as the time threshold, and the operation performance data of the online education platform within the time threshold is obtained, and the operation performance data includes the jamming frequency and the average duration of page switching, and the jamming frequency and the average duration of page switching are compared and analyzed with the preset jamming frequency threshold and the preset average duration threshold of page switching stored internally. If the jamming frequency is greater than the preset jamming frequency threshold, or the average duration of page switching is greater than the preset average duration threshold of page switching, a management signal is generated, and then the frequency of generating management signals of the online education platform within the time threshold is obtained, and the frequency of generating management signals of the online education platform is set as the operation evaluation coefficient. It should be noted that the operation evaluation coefficient is a comprehensive quantitative evaluation coefficient reflecting the operation status of the online education platform. The larger the value of the operation evaluation coefficient, the greater the risk of abnormal operation status of the online education platform. Compare and analyze the operation evaluation coefficient with the preset operation evaluation coefficient range stored internally: If the operation evaluation coefficient is greater than the maximum value of the ratio between the preset operation evaluation coefficient intervals, a normal signal is generated and sent to the information verification unit; If the ratio between the operation evaluation coefficient and the preset operation evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated and sent to the online management unit. After receiving the alarm signal, the online management unit immediately displays the preset warning text corresponding to the alarm signal, so as to rationally manage the online education platform according to the feedback text, so as to improve the fluency of information reading on the online education platform and improve the user experience; After receiving the normal signal, the information verification unit further collects the information display data of the online education platform, that is, the information verification unit further supervises the optimization based on the information display of the online education platform, so as to provide timely feedback on the problems after optimization, so as to improve the optimization effect of the online education platform, and conducts information integrity supervision, evaluation and analysis on the information display data. The specific information integrity supervision, evaluation and analysis process is as follows: The time threshold is divided into i sub-time periods, where i is a natural number greater than zero, and the information display data of the online education platform in each sub-time period is obtained, the information display data including the defect interference value and the reading influence value, and the defect interference value and the reading influence value are compared and analyzed with the stored preset defect interference value threshold and the preset reading influence value threshold. If the defect interference value is greater than or equal to the preset defect interference value threshold, or the reading influence value is greater than or equal to the preset reading influence value threshold, the corresponding sub-time period is set as the optimized legacy interference time period, the ratio between the number of optimized legacy interference time periods and the total number of sub-time periods is obtained, and it is set as the legacy interference coefficient, and the legacy interference coefficient is judged and processed: If the legacy interference coefficient is less than a preset legacy interference coefficient threshold, a stabilization signal is generated and sent to the optimization evaluation unit; If the legacy interference coefficient is greater than or equal to the preset legacy interference coefficient threshold, an interference signal is generated and sent to the online management unit. After receiving the interference signal, the online management unit immediately displays the preset warning text corresponding to the interference signal, so as to provide timely feedback on the optimized problem according to the feedback text, so as to improve the optimization effect of the online education platform; In the embodiment of the present invention, the defect interference value indicates the duration corresponding to the information defect in the page in the page feature video of the online education platform in the sub-time period. It should be noted that the analysis is performed from the perspective of information integrity to ensure the optimization effect and optimization stability of the online education platform; In the embodiment of the present invention, the reading impact value indicates the duration of overlap corresponding to the jitter and ripples in the page feature video of the online education platform within the sub-time period. It should be noted that the reading impact value is a reflection of the interference phenomenon existing in the use of the online education platform after optimization, so as to further improve the problems existing after optimization. Embodiment 2

[0015] After receiving the code operation data, the content evaluation unit immediately conducts virtual operation supervision evaluation and analysis on the code operation data, so as to understand the optimization of the online education platform from the perspective of code operation, so as to improve the operation stability and speed of the online education platform. The specific virtual operation supervision evaluation and analysis process is as follows: The code running data of the online education platform within the time threshold is obtained. The code running data represents the code running speed curve. The code optimization evaluation coefficient is obtained by analyzing the curve information of the code running speed curve, and the code optimization evaluation coefficient is compared and analyzed with the preset code optimization evaluation coefficient threshold stored in the internal storage: If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is less than 1, a qualified signal is generated and sent to the optimization evaluation unit; If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is greater than or equal to 1, a failure signal is generated and sent to the online management unit. After receiving the failure signal, the online management unit immediately displays the preset warning text corresponding to the failure signal, so as to perform targeted code management on the online education platform according to the feedback text, so as to improve the operation stability and speed of the online education platform; In an embodiment of the present invention, the curve information analysis process of the code running speed curve is as follows: the code running speed curve is collected once every t1 time to obtain the code running speed, and a set A1 of the code running speed is constructed; the code running speed curve is collected once every t2 time to obtain the code running speed, and a set A2 of the code running speed is constructed; and so on, the code running speed curve is collected once every tg time to obtain the code running speed, and a set Ag of the code running speed is constructed, t1, t2...tg are not equal, g is a natural number greater than zero, and then the mean values ​​corresponding to the sets A1, A2...Ag are obtained, and the difference between the maximum value and the minimum value in the mean values ​​corresponding to the sets A1, A2...Ag is further obtained, and it is set as the code optimization evaluation coefficient. It should be noted that the value of the code optimization evaluation coefficient is an influencing parameter reflecting the optimization efficiency evaluation of the online education platform, and the mean values ​​corresponding to the sets A1, A2...Ag are obtained by calculating the values ​​in the sets; After receiving the stable signal and the qualified signal, the optimization evaluation unit immediately conducts optimization long-term supervision evaluation analysis, that is, through information feedback, it understands the sustainability of the optimization effect corresponding to the optimization decision of the online education platform, so as to adjust the current optimization decision to improve the sustainability and long-term effectiveness of the optimization operation effect of the online education platform. The specific optimization long-term supervision evaluation analysis process is as follows: The operation evaluation coefficient corresponding to the normal signal of the online education platform within the time threshold is obtained, and the code optimization evaluation coefficient corresponding to the qualified signal of the online education platform within the time threshold is obtained, and the legacy interference coefficient corresponding to the stable signal of the online education platform within the time threshold is obtained. The operation evaluation coefficient, the code optimization evaluation coefficient and the legacy interference coefficient are labeled as YP, DY and YL respectively, and the operation evaluation coefficient YP, the code optimization evaluation coefficient DY and the legacy interference coefficient YL are substituted into the formula The platform optimization evaluation coefficient is obtained, wherein a1, a2 and a3 are preset proportional factor coefficients of the operation evaluation coefficient, the code optimization evaluation coefficient and the legacy interference coefficient respectively, and the proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate, a4 is the preset correction factor coefficient, a1, a2, a3 and a4 are all greater than zero, and R is the platform optimization evaluation coefficient. It should be noted that in order to more intuitively reflect the optimization status of the online education platform, through the comprehensive quantitative evaluation method, the data from different aspects are combined through the corresponding calculation method to improve the comparability between the data; Then, the duration between the end of the online education platform optimization and the time when the platform optimization evaluation coefficient is equal to the preset platform optimization evaluation coefficient threshold is obtained, and it is set as the optimization efficiency continuity coefficient, and the optimization efficiency continuity coefficient is compared and analyzed with the preset optimization efficiency continuity coefficient threshold stored in the internal data: If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is greater than or equal to 1, no signal is generated; If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is less than 1, a control signal is generated and sent to the online management unit. After receiving the control signal, the online management unit immediately displays the preset warning text corresponding to the control signal, so as to adjust the current online education platform optimization decision to improve the sustainability and long-term effectiveness of the optimization operation effect of the online education platform; In summary, the present invention analyzes from the perspective of the post-optimization operation process to understand the optimization effect of the online education platform, that is, initially performs visual operation supervision and evaluation analysis on the operation performance data from a visual perspective to understand the operation status of the online education platform after optimization, so as to rationally manage the online education platform according to the feedback text, so as to improve the fluency of information reading on the online education platform and improve the user experience. At the same time, virtual operation supervision and evaluation analysis is performed on the code operation data from an invisible perspective to understand the optimization status of the online education platform from the perspective of code operation, so as to improve the operation stability and speed of the online education platform. By means of information feedback, information integrity supervision and evaluation analysis is further performed on the information display data from a visual perspective, that is, the optimization is further supervised from the information display status of the online education platform, so as to provide timely feedback on the problems existing after optimization, so as to improve the optimization effect of the online education platform. By means of information feedback and fusion, optimization long-term supervision and evaluation analysis is performed to understand the sustainability of the optimization effect corresponding to the optimization decision of the online education platform, so as to adjust the current optimization decision, so as to improve the sustainability and long-term effectiveness of the optimization operation effect of the online education platform.

[0016] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0017] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A university online education platform management system based on cloud big data, characterized by: It includes online education management center, data collection unit, page supervision unit, content evaluation unit, information verification unit, optimization evaluation unit and online management unit; The data collection unit is used to collect the operation performance data and code operation data of the online education platform, and send the operation performance data and code operation data to the page supervision unit and the content evaluation unit respectively; Upon receiving the operation performance data, the page supervision unit immediately performs a visual operation supervision evaluation analysis on the operation performance data, performs a comparison process on the obtained operation evaluation coefficients, and obtains a normal signal and an alarm signal; The information verification unit is used to respond to the normal signal to collect the information display data of the online education platform, and to perform information integrity supervision, evaluation and analysis on the information display data, and to compare and process the obtained residual interference coefficient to obtain a stable signal and an interference signal; After receiving the code operation data, the content evaluation unit immediately performs virtual operation supervision evaluation analysis on the code operation data, compares the obtained code optimization evaluation coefficients, and obtains qualified signals and unqualified signals; The optimization and evaluation unit optimizes the long-term regulatory evaluation and analysis through information feedback and information fusion, and sends the obtained control signals to the online management unit.

2. The university online education platform management system based on cloud big data according to claim 1 is characterized in that: The visibility operation supervision evaluation and analysis process of the page supervision unit is as follows: The duration corresponding to the set operation period of the online education platform after optimization is collected and set as the time threshold, and the operation performance data of the online education platform within the time threshold is obtained, the operation performance data includes the jamming frequency and the average duration of page switching, and the jamming frequency and the average duration of page switching are compared and analyzed with the preset jamming frequency threshold and the preset average duration threshold of page switching that are internally recorded and stored. If the jamming frequency is greater than the preset jamming frequency threshold, or the average duration of page switching is greater than the preset average duration threshold of page switching, a management signal is generated, and then the frequency of generating management signals of the online education platform within the time threshold is obtained, and the frequency of generating management signals of the online education platform is set as the operation evaluation coefficient; Compare and analyze the operation evaluation coefficient with the preset operation evaluation coefficient range stored internally: If the operation evaluation coefficient is greater than the maximum value of the ratio between the preset operation evaluation coefficient intervals, a normal signal is generated; If the ratio between the operation evaluation coefficient and the preset operation evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated.

3. The university online education platform management system based on cloud big data according to claim 1 is characterized in that: The information integrity supervision assessment and analysis process of the information verification unit is as follows: The time threshold is divided into i sub-time periods, where i is a natural number greater than zero, and the information display data of the online education platform in each sub-time period is obtained, the information display data including the defect interference value and the reading influence value, and the defect interference value and the reading influence value are compared and analyzed with the stored preset defect interference value threshold and the preset reading influence value threshold. If the defect interference value is greater than or equal to the preset defect interference value threshold, or the reading influence value is greater than or equal to the preset reading influence value threshold, the corresponding sub-time period is set as the optimized legacy interference time period, the ratio between the number of optimized legacy interference time periods and the total number of sub-time periods is obtained, and it is set as the legacy interference coefficient, and the legacy interference coefficient is judged and processed: If the legacy interference coefficient is less than a preset legacy interference coefficient threshold, a stable signal is generated; If the legacy interference coefficient is greater than or equal to a preset legacy interference coefficient threshold, an interference signal is generated.

4. The university online education platform management system based on cloud big data according to claim 3 is characterized in that: The defect interference value indicates the duration of information defects on a page in a page feature video of an online education platform within a sub-time period; the reading impact value indicates the duration of overlap of jitter and ripples on a page in a page feature video of an online education platform within a sub-time period.

5. The university online education platform management system based on cloud big data according to claim 1 is characterized in that: The virtual operation supervision evaluation and analysis process of the content evaluation unit is as follows: The code running data of the online education platform within the time threshold is obtained. The code running data represents the code running speed curve. The code optimization evaluation coefficient is obtained by analyzing the curve information of the code running speed curve, and the code optimization evaluation coefficient is compared and analyzed with the preset code optimization evaluation coefficient threshold stored in the internal storage: If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is less than 1, a qualified signal is generated; If the ratio between the code optimization evaluation coefficient and the preset code optimization evaluation coefficient threshold is greater than or equal to 1, a failure signal is generated.

6. The university online education platform management system based on cloud big data according to claim 5 is characterized in that: The curve information analysis process of the code running speed curve is as follows: The code running speed curve is collected once every t1 time period to obtain the code running speed, and a set A1 of code running speeds is constructed. The code running speed curve is collected once every t2 time period to obtain the code running speed, and a set A2 of code running speeds is constructed. The code running speed curve is collected once every tg time period to obtain the code running speed, and a set Ag of code running speeds is constructed. t1, t2...tg are not equal, g is a natural number greater than zero, and then the means corresponding to the sets A1, A2...Ag are obtained, and the difference between the maximum value and the minimum value in the means corresponding to the sets A1, A2...Ag is further obtained, and it is set as the code optimization evaluation coefficient.

7. The university online education platform management system based on cloud big data according to claim 1 is characterized in that: The optimization and long-term regulatory evaluation and analysis process of the optimization and evaluation unit is as follows: The operation evaluation coefficient corresponding to the normal signal of the online education platform within the time threshold is obtained, and the code optimization evaluation coefficient corresponding to the qualified signal of the online education platform within the time threshold is obtained, and the legacy interference coefficient corresponding to the stable signal of the online education platform within the time threshold is obtained. The operation evaluation coefficient, the code optimization evaluation coefficient and the legacy interference coefficient are labeled as YP, DY and YL respectively, and the operation evaluation coefficient YP, the code optimization evaluation coefficient DY and the legacy interference coefficient YL are substituted into the formula Obtaining a platform optimization evaluation coefficient, wherein a1, a2, and a3 are preset proportional factor coefficients of the operation evaluation coefficient, the code optimization evaluation coefficient, and the legacy interference coefficient, respectively, a4 is a preset correction factor coefficient, a1, a2, a3, and a4 are all greater than zero, and R is the platform optimization evaluation coefficient; Then, the duration between the end of the online education platform optimization and the time when the platform optimization evaluation coefficient is equal to the preset platform optimization evaluation coefficient threshold is obtained, and it is set as the optimization efficiency continuity coefficient, and the optimization efficiency continuity coefficient is compared and analyzed with the preset optimization efficiency continuity coefficient threshold stored in the internal data: If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is greater than or equal to 1, no signal is generated; If the ratio between the optimization efficiency continuity coefficient and the preset optimization efficiency continuity coefficient threshold is less than 1, a control signal is generated.

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