Information management method and system based on online education system

Abstract

The invention relates to the technical field of online education data processing, and particularly discloses an information management method and system based on an online education system, and the method comprises the steps: obtaining a corresponding image collection quality evaluation factor through analyzing the image collection information of teaching equipment which the online education system belongs to; therefore, the image acquisition information of the teaching equipment to which the online education system belongs can be managed and fed back, and the audio acquisition quality evaluation factor of the teaching equipment to which the online education system belongs is obtained by analyzing the audio acquisition information of the teaching equipment to which the online education system belongs. Therefore, the audio acquisition information of the teaching equipment to which the online education system belongs can be managed and fed back, and finally the network quality index of the teaching network to which the online education system belongs is evaluated, so that the initiative and timeliness of teaching equipment management are enhanced, the overall performance of the online education system is improved, and the user experience is improved. And a solid foundation is laid for providing a high-quality online teaching environment.

Description

Technical Field

[0001] The present invention relates to the technical field of online education data processing, and specifically to an information management method and system based on an online education system. Background Art

[0002] In the process of online education information management, the performance indicators of teaching equipment are crucial to ensuring the fluency and interactivity of online learning. However, the existing online education management methods do not include teaching equipment performance indicators in the evaluation scope, resulting in the inability to timely discover and solve teaching problems caused by insufficient equipment performance, further misleading the direction of online teaching improvement, and ultimately affecting the effectiveness of the entire online education system.

[0003] For example, the invention patent with announcement number CN113570484B announces an online primary school education management system and method based on big data, which relates to the field of online education management technology. The online primary school education management system includes a management platform for managing online education; a teacher terminal for teachers to conduct online teaching; a student terminal for students to conduct online learning; a processor for processing data from the teacher terminal and the learning terminal; the teacher terminal, the student terminal and the processor together constitute an online education platform; by providing an information reading module, information on the gaze point of students during online learning can be read.

[0004] For example, the invention patent with announcement number CN116137012B announces an online teaching quality supervision management system based on Internet education, which involves the field of online education technology; it includes a server, as well as a data acquisition module, a quality analysis module and a quality assessment module; the data acquisition module is used to collect teaching interaction information between online teaching terminals and student terminals during the teaching cycle, and perform data analysis and processing of teaching behaviors based on the teaching interaction information, analyze the teaching behavior value Gi, and transmit the teaching behavior value Gi to the quality analysis module via the server; in the process of online teaching quality supervision of Internet education, comprehensive processing is performed on online teaching time data, online teacher-student interaction time data, online student absence time data, online teaching network quality data and student offline make-up time data.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: when evaluating the quality of online teaching, the existing technology only focuses on analyzing the network quality and interaction status of online teaching, which makes it difficult to discover potential technical failures and performance bottlenecks of online teaching in the online education system, which may interrupt the online teaching process and affect the fluency and stability of online teaching activities, thereby having a significant negative impact on the quality of online teaching in the online education system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an information management method and system based on an online education system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an information management method based on an online education system, including: step one, obtaining image acquisition information of the teaching equipment belonging to the online education system, analyzing the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and comparing it with the image acquisition quality assessment threshold; if the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, executing step two; if the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, managing and feedback the image acquisition information of the teaching equipment belonging to the online education system; step two, acquiring the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and comparing it with the image acquisition quality assessment threshold ... and step three, acquiring the image acquisition quality assessment factor of the teaching equipment belonging to the online education system. The audio collection information of the teaching equipment belonging to the education system is analyzed, and the audio collection quality assessment factor of the teaching equipment belonging to the online education system is analyzed, and compared with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, step three is executed. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, management feedback is given to the audio collection information of the teaching equipment belonging to the online education system; step three, collect network performance parameters of the teaching network belonging to the online education system, evaluate the network quality index of the teaching network belonging to the online education system, and thereby provide management feedback on the information management of the online education system.

[0008] As a further method, the image acquisition information of the teaching equipment belonging to the online education system is managed and fed back. The specific feedback process is: the image acquisition information of the teaching equipment belonging to the online education system is transmitted to the first parameter set in the information management database, and a first parameter set report is generated, thereby managing and feeding back the image acquisition information of the teaching equipment belonging to the online education system.

[0009] As a further method, the audio collection information of the teaching equipment belonging to the online education system is managed and fed back. The specific feedback process is: the audio collection information of the teaching equipment belonging to the online education system is transmitted to the second parameter set in the information management database, and a second parameter set report is generated, thereby managing and feeding back the audio collection information of the teaching equipment belonging to the online education system.

[0010] As a further method, the information management of the online education system is managed with feedback. The specific feedback process is: comparing the network quality index of the teaching network to which the online education system belongs with the network quality threshold. If the network quality index of the teaching network to which the online education system belongs is less than or equal to the network quality threshold, management feedback is performed on the information management of the online education system.

[0011] The second aspect of the present invention provides a system for an application such as an information management method based on an online education system, including: an image information analysis module, which is used to obtain image acquisition information of teaching equipment belonging to the online education system, analyze the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the image acquisition quality assessment threshold. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, the audio information analysis module is executed. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, the image acquisition information of the teaching equipment belonging to the online education system is managed and fed back; the audio information analysis module is used to acquire image acquisition information of the online education system; The audio collection information of the teaching equipment belongs to the online education system, and the audio collection quality assessment factor of the teaching equipment belonging to the online education system is analyzed, and compared with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, the network quality assessment module is executed. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, the audio collection information of the teaching equipment belonging to the online education system is managed and feedback is given; the network quality assessment module is used to collect network performance parameters of the teaching network belonging to the online education system, evaluate the network quality index of the teaching network belonging to the online education system, and thereby provide management feedback on the information management of the online education system.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides an information management method and system based on an online education system. By analyzing the image acquisition information of the teaching equipment belonging to the online education system, the corresponding image acquisition quality evaluation factor is obtained, thereby managing and feedbacking the image acquisition information of the teaching equipment belonging to the online education system. By analyzing the audio acquisition information of the teaching equipment belonging to the online education system, the audio acquisition quality evaluation factor of the teaching equipment belonging to the online education system is obtained, thereby managing and feedbacking the audio acquisition information of the teaching equipment belonging to the online education system. Finally, the network quality index of the teaching network belonging to the online education system is evaluated, which enhances the initiative and timeliness of the teaching equipment management, helps to improve the overall performance of the online education system, and lays a solid foundation for providing a high-quality online teaching environment.

[0013] (2) By analyzing the image acquisition quality assessment factor and the audio acquisition quality assessment factor from the teacher's end, the present invention can accurately quantify the performance of the teaching equipment of the online education system and the operating status of the audio equipment, ensuring that the visual quality and auditory quality of online teaching meet high standards, so that problems in the image and audio acquisition of online teaching equipment can be discovered and solved in a timely manner, thereby reducing the decline in online teaching quality caused by equipment failure or insufficient performance, further promoting the continuous optimization and upgrading of online teaching equipment, and laying a solid foundation for the stable operation and high-quality development of the online education system.

[0014] (3) The present invention obtains the network quality index of the teaching network of the online education system by comprehensively analyzing the data transmission volume, network transmission rate change rate deviation value and network transmission rate maximum fluctuation value of the teaching network of the online education system during the network performance detection period, so as to continuously optimize and improve the network environment of online education, so as to ensure that the audio and image information of the teacher end can be stably and efficiently transmitted to the student end, thereby fundamentally enhancing the overall quality of online teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0016] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0017] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0018] Figure 3 The figure is a schematic diagram of a time domain waveform of a certain audio segment involved in the present invention.

[0019] Figure 4 It is a schematic diagram of a network transmission rate curve involved in the present invention.

[0020] Figure markings: 1. Maximum amplitude; 2. Minimum amplitude; 3. Start time point of the network performance detection cycle; 4. End time point of the network performance detection cycle; 5. Lowest point of the curve; 6. Highest point of the curve. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides an information management method based on an online education system, including: step one, obtaining image acquisition information of the teaching equipment belonging to the online education system, analyzing the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and comparing it with the image acquisition quality assessment threshold; if the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, executing step two; if the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, performing management feedback on the image acquisition information of the teaching equipment belonging to the online education system.

[0023] It needs to be explained that the information management method based on the online education system is aimed at teachers to ensure that the online teaching content is presented in the most optimized state. Therefore, this method can guide teachers to carry out debugging and preparation before the online teaching live broadcast.

[0024] It needs to be explained that the above-mentioned image acquisition quality assessment threshold represents the minimum value of the reasonable range of the image acquisition quality assessment factor of the teaching equipment of the online education system, which is formulated by the visual quality analyst based on the actual situation. If the image acquisition quality assessment factor of the teaching equipment of the online education system is greater than the image acquisition quality assessment threshold, it indicates that the image acquisition quality of the teaching equipment of the online education system meets the expected standards, and the acquired audio quality can continue to be evaluated. If the image acquisition quality assessment factor of the teaching equipment of the online education system is less than or equal to the image acquisition quality assessment threshold, it indicates that the image acquisition quality of the teaching equipment of the online education system does not meet the expected standards, and management feedback is required for the image acquisition information of the teaching equipment of the online education system.

[0025] It needs to be further explained that the above-mentioned acquisition of the image acquisition information of the teaching equipment belonging to the online education system refers to the online education system receiving the image acquisition information of the teaching equipment, and the teaching equipment and the hardware belonging to the online education system (such as a computer) transmit information through a wireless connection (such as Bluetooth), wherein the image acquisition information of the teaching equipment belonging to the online education system includes the image acquisition information of each acquired image of the teaching equipment belonging to the online education system within the image analysis cycle, wherein the data in the image acquisition information includes the color value of each image data analysis point, the number of height pixels and width pixels of each acquired image and the grayscale value of each pixel point. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters, and the direction in which these parameters can be fed back (i.e., positive and negative relationships) is consistent.

[0026] Specifically, the management feedback of the image acquisition information of the teaching equipment belonging to the online education system is performed, and the specific feedback process is: The image acquisition information of the teaching equipment belonging to the online education system is transmitted to the first parameter set in the information management database, and a first parameter set report is generated, thereby managing and feedbacking the image acquisition information of the teaching equipment belonging to the online education system; the above-mentioned first parameter set report is a document for storing the image acquisition information of the teaching equipment belonging to the online education system, that is, a document storing the first parameter set, which is compiled by the automatic document generation function in the online education system; in an example embodiment, the information in the above-mentioned first parameter set report includes image resolution of 1920x1080 pixels, full HD; frame rate of 30 frames / second; light adaptability: support automatic exposure and white balance adjustment; installation position and angle: center front of the classroom, elevation angle of 15 degrees; based on the above information, the information technology administrator adjusts the acquisition of teaching images by the teaching equipment, and can adjust the resolution and frame rate, etc., so that the image acquisition quality assessment factor of the teaching equipment belonging to the online education system can be re-analyzed and feedback can be performed until the image acquisition quality assessment factor is greater than the image acquisition quality assessment threshold.

[0027] In an exemplary embodiment, the teaching device includes a camera (used to collect image acquisition information of the teaching device belonging to the online education system) and an external microphone (used to collect audio acquisition information of the teaching device belonging to the online education system).

[0028] Furthermore, the image acquisition quality assessment factor of the teaching equipment of the online education system is analyzed, and the specific analysis process is as follows: The image acquisition information of each acquired image of the teaching equipment of the online education system within the image analysis period is extracted from the image acquisition information of the teaching equipment of the online education system, the color value of each image data analysis point is located and extracted from the image acquisition information of each acquired image, the reference color value of each image data analysis point of each acquired image is obtained, and the color deviation value of each acquired image is obtained by performing data processing with the color value of each image data analysis point; the above-mentioned image analysis period is a time period for analyzing the image acquisition quality of the teaching equipment of the online education system, and its specific duration is determined by the visual quality analyst according to actual conditions; the above-mentioned image data analysis points refer to a number of image quality analysis position points randomly arranged on each acquired image by the online education system using an automated algorithm (such as a random sampling algorithm); the color value of the above-mentioned image data analysis point refers to converting the numerical value part of the hexadecimal color code of the point into a decimal value, and the numerical value is recorded as the color value of the image data analysis point. In an example embodiment, a certain The image data analysis point is analyzed by the color analysis software Red Spider 5 to obtain its hexadecimal color code #000010, and its numerical part is converted into decimal 16, so the color value of the point is 16; the reference color value of each image data analysis point of each collected image is set by the visual quality analyst to set the comparison image acquisition device (such as a high-definition camera) of the teaching equipment, and the image data is collected simultaneously with the teaching equipment, and the image of the comparison image acquisition device is not transmitted to the online education system, and the built-in solid-state hard disk data of the high-definition camera is directly read through professional image processing software (such as digital asset management software), so that the original image data without any processing can be obtained. These data can best reflect the real color and details of the actual shooting scene. Therefore, the color value of each image data analysis point of each collected image of the built-in solid-state hard disk data of the high-definition camera is recorded as the reference color value of each image data analysis point of each collected image; in an example embodiment, the specific data processing process of the color deviation value of a certain collected image is: , The color value of the b-th image data analysis point, is the reference color value of the bth image data analysis point, b is the number of each image data analysis point, , j is the total number of image data analysis points.

[0029] The height pixel number and width pixel number of each acquired image are extracted from the image acquisition information of each acquired image, and the product is processed to obtain the total number of pixels of each acquired image; the height pixel number and width pixel number of each acquired image, in an example embodiment, the resolution of a certain acquired image is obtained by querying the image properties of a certain acquired image and obtaining that the resolution is 1920x1080, then the height pixel number is 1080 pixels, and the width pixel number is 1920.

[0030] The grayscale value of each pixel is extracted from the image acquisition information of each acquired image, and data processing is performed with the height pixel number and the width pixel number of each acquired image to obtain the grayscale variance value of each acquired image. In an exemplary embodiment, the grayscale variance value of a certain acquired image, the specific data processing process is: first, an online image conversion tool (such as an image converter) is used to convert a certain acquired image into a corresponding grayscale image (an image that only contains brightness information but not color information), and all pixels in the grayscale image are traversed by the online image conversion tool to obtain the grayscale value of each pixel, and the average value of all pixel values ​​is calculated by the following expression: ,in is the grayscale value of the grayscale image at the (z, x) position, L is the number of pixels of the height of the grayscale image, and W is the number of pixels of the width of the grayscale image. , z is the number of pixels in the width direction, , x is the number of each pixel in the height direction; similarly, traverse each pixel in the grayscale image, calculate the square of the difference between the grayscale value of each pixel and the grayscale mean, find the sum of these square differences, and divide it by the total number of pixels (W*L) to get the grayscale variance value. The specific expression is: , BD is the grayscale variance value of a certain acquired image. The larger the grayscale variance value, the higher the discrete degree of the pixel grayscale value in the image, that is, the clearer the image.

[0031] The image acquisition quality evaluation factor of the teaching equipment of the online education system is obtained by comprehensive analysis. In this embodiment, the image acquisition quality evaluation factor of the teaching equipment of the online education system is obtained by comprehensive analysis of the color deviation value, the total number of pixels and the grayscale variance value of each acquired image. It is used to evaluate the quality of the image acquired by the teaching equipment received by the online education system. For example, the online education system receives the teacher's chemical experiment demonstration image acquired by the camera. The specific expression is: ; is the color deviation value of the mth collected image, which is usually used to quantify the color value difference between the color distribution in the collected image and the reference color value distribution.

[0032] is the total number of pixels in the mth acquired image, which directly reflects the size and detail of the acquired image.

[0033] is the grayscale variance value of the mth collected image. The larger the grayscale variance value, the more drastic the brightness change in the collected image is, the more details or textures it contains, and the more clearly it can reflect the actual teaching environment and actual teaching content.

[0034] m is the number of each collected image, , n is the total number of collected images, e is a natural constant, It is the influence factor corresponding to the color deviation value unit value preset in the information management database, which represents the influence degree of the color deviation value unit value on the image acquisition quality assessment factor. When used, the influence factor corresponding to the color deviation value unit value can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the color deviation value and the influence factor corresponding to the color deviation value unit value preset in the information management database form a mapping set, and the real-time color deviation value is input into the mapping set to obtain the influence factor corresponding to the color deviation value unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0035] It is the influence factor corresponding to the unit value of the total number of pixels preset in the information management database, which represents the influence degree of the unit value of the total number of pixels on the image acquisition quality assessment factor. When used, the influence factor corresponding to the unit value of the total number of pixels can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the total number of pixels and the influence factors corresponding to the unit value of the total number of pixels preset in the information management database form a mapping set, and the real-time total number of pixels is input into the mapping set to obtain the influence factor corresponding to the unit value of the total number of pixels. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0036] It is the influence factor corresponding to the grayscale variance value unit value preset in the information management database, which represents the influence degree of the grayscale variance value unit value on the image acquisition quality assessment factor. When used, the influence factor corresponding to the grayscale variance value unit value can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the grayscale variance value and the influence factor corresponding to the grayscale variance value unit value preset in the information management database form a mapping set, and the real-time grayscale variance value is input into the mapping set to obtain the influence factor corresponding to the grayscale variance value unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0037] in, It is an evaluation factor of the image acquisition quality of the teaching equipment of the online education system. If the resolution of the acquired image is low, that is, the pixels per unit area of ​​the acquired image are small, this directly reflects the insufficient accuracy of the teaching equipment in the image acquisition process. The low-resolution acquired image cannot fully capture and reproduce the detailed information in the actual teaching scene, resulting in a fuzzy or rough visual effect of the acquired image, which is specifically manifested in the grayscale distribution of the acquired image, that is, the grayscale variance value is relatively small. The grayscale variance is an important indicator for measuring the contrast of the acquired image. Low grayscale variance means that the difference between different grayscale levels in the acquired image is not significant enough, and the contrast is reduced, which limits the layering and detail expression of the acquired image. Furthermore, due to the fuzziness and lack of contrast of the acquired image itself, the color deviation between the acquired image and the actual scene or reference image will increase. In summary, the low-resolution acquired image not only shows a reduction in the total number of pixels and a decrease in image clarity, but also further leads to a decrease in the grayscale variance value and an increase in the color deviation value. These factors work together to significantly reduce the image acquisition quality and fail to meet the needs of high-quality online teaching. Therefore, in the process of improving the quality of online teaching, a comprehensive analysis of the three parameters can quantify the image acquisition capabilities of the teaching equipment to ensure that the acquired images can clearly and accurately reflect the actual teaching scene and improve the quality of online teaching in the online education system.

[0038] Step 2. Collect the audio collection information of the teaching equipment belonging to the online education system, analyze the audio collection quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, execute step 3. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, manage feedback on the audio collection information of the teaching equipment belonging to the online education system.

[0039] It needs to be explained that the above-mentioned audio collection quality assessment threshold represents the minimum value of the reasonable range of the audio collection quality assessment factor of the teaching equipment of the online education system, which is formulated by the audio quality analyst based on actual conditions. If the audio collection quality assessment factor of the teaching equipment of the online education system is greater than the audio collection quality assessment threshold, it indicates that the audio collection quality of the teaching equipment of the online education system meets the expected standards, and the quality of the teaching network of the online education system can continue to be evaluated. If the audio collection quality assessment factor of the teaching equipment of the online education system is less than or equal to the audio collection quality assessment threshold, it indicates that the audio collection quality of the teaching equipment of the online education system does not meet the expected standards, and management feedback is required for the audio collection information of the teaching equipment of the online education system.

[0040] It needs to be further explained that the above-mentioned audio collection information of the teaching equipment belonging to the online education system refers to the online education system receiving the audio collection information of the teaching equipment, and the teaching equipment and the hardware belonging to the online education system (such as a computer) transmit information through a wireless connection (such as Bluetooth), wherein the audio collection information of the teaching equipment belonging to the online education system includes the audio collection information of each segment of collected audio of the teaching equipment belonging to the online education system within the audio analysis cycle, wherein the data in the audio collection information includes the time domain waveform of each segment of collected audio, the amplitude of each audio analysis data point and the collection start time point of each segment of collected audio. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters, and the direction in which these parameters can be fed back (i.e., the positive and negative relationship) is consistent.

[0041] In a specific embodiment, the present invention can accurately quantify the performance of the teaching equipment belonging to the online education system and the operating status of the audio equipment by analyzing the image acquisition quality assessment factor and the audio acquisition quality assessment factor from the teacher's side, thereby ensuring that the visual quality and auditory quality of online teaching meet high standards, so that problems in the image and audio acquisition of online teaching equipment can be discovered and solved in a timely manner, thereby reducing the decline in online teaching quality caused by equipment failure or insufficient performance, further promoting the continuous optimization and upgrading of online teaching equipment, and laying a solid foundation for the stable operation and high-quality development of the online education system.

[0042] Specifically, the management feedback of the audio collection information of the teaching equipment belonging to the online education system is performed, and the specific feedback process is: The audio collection information of the teaching equipment belonging to the online education system is transmitted to the second parameter set in the information management database, and a second parameter set report is generated, thereby managing and feedbacking the audio collection information of the teaching equipment belonging to the online education system; the above-mentioned second parameter set report is a document for storing the audio collection information of the teaching equipment belonging to the online education system, that is, a document storing the second parameter set, which is compiled by the automatic document generation function in the online education system; in an example embodiment, the information in the above-mentioned second parameter set report includes a sampling rate of 44.1 kHz, a quantization bit of 16 bits, and a number of channels of dual channels; based on the above information, the information technology administrator adjusts the collection of teaching audio by the teaching equipment, and can adjust the sampling rate and the number of channels, etc., so that the audio collection quality assessment factor of the teaching equipment belonging to the online education system can be re-analyzed and feedback can be given until the audio collection quality assessment factor is greater than the audio collection quality assessment threshold.

[0043] Furthermore, the analysis is performed to obtain the audio collection quality assessment factor of the teaching equipment of the online education system. The specific analysis process is as follows: Extract the audio collection information of each segment of the audio collected by the teaching equipment of the online education system within the audio analysis period from the audio collection information of the teaching equipment of the online education system, extract the time domain waveform of each segment of the audio collected from the audio collection information of each segment of the audio collected, extract the maximum amplitude and the minimum amplitude of each segment of the audio collected from the time domain waveform of each segment of the audio collected, perform difference processing to obtain the maximum amplitude difference of each segment of the audio collected, obtain the reference time domain waveform of each segment of the audio collected, and analyze to obtain the maximum reference amplitude difference of each segment of the audio collected; the above-mentioned audio analysis period is a time period for analyzing the audio collection quality of the teaching equipment of the online education system, and its specific duration is determined by the audio quality analyst according to the actual situation; the time domain waveform of each segment of the audio collected is obtained by processing each segment of the audio collected within the audio analysis period through an audio processing tool (such as split-second frame); the maximum amplitude difference of each segment of the audio collected, such as Figure 3 As shown, it is a time domain waveform diagram of a certain audio segment, which shows the relationship between the amplitude of the audio and the audio analysis time point. The horizontal axis is the audio analysis time point in milliseconds, and the vertical axis is the amplitude in percentage. The maximum amplitude 1 of the segment of collected audio minus the minimum amplitude 2 is the maximum amplitude difference of the segment of collected audio; the reference time domain waveform diagram of each segment of collected audio refers to the result of denoising the time domain wave of each segment of collected audio through a denoising algorithm (such as Wiener filtering method) and marking it as the reference time domain waveform diagram of each segment of collected audio; the reference maximum amplitude difference of each segment of collected audio is consistent with the method of obtaining the maximum amplitude difference of each segment of collected audio.

[0044] The amplitude of each audio analysis data point is located and extracted from the time domain waveform of each segment of collected audio, and the reference amplitude of each audio analysis data point is extracted from the reference time domain waveform of each segment of collected audio, and data processing is performed with the amplitude of each audio analysis data point to obtain the total amplitude deviation of each segment of collected audio; the above-mentioned audio analysis data points are data points randomly arranged by the audio quality analyst in the time domain waveform of each segment of collected audio; the above-mentioned total amplitude deviation of each segment of collected audio refers to the amplitude of each audio analysis data point minus the corresponding reference amplitude and performing absolute value processing, and the processed values ​​are accumulated to obtain the total amplitude deviation of each segment of collected audio.

[0045] The collection start time point of each segment of collected audio is extracted from the audio collection information of each segment of collected audio, and the reference start time point of each segment of collected audio is obtained, and the difference processing is performed with the corresponding collection start time point of each segment of collected audio to obtain the collection delay duration of each segment of collected audio; the collection start time point of each segment of collected audio is extracted from the data recorded by the timestamp function of the teaching equipment; the reference start time point of each segment of collected audio is collected by the audio quality analyst according to the actual collection situation of each segment of collected audio; the collection delay duration of each segment of collected audio refers to the reference start time point minus the collection start time point.

[0046] The audio acquisition quality evaluation factor of the teaching equipment of the online education system is obtained through comprehensive analysis. In this embodiment, the audio acquisition quality evaluation factor of the teaching equipment of the online education system is obtained through comprehensive analysis of the maximum amplitude difference of each segment of collected audio, the maximum reference amplitude difference, the total amplitude deviation value and the acquisition delay time. It is used to evaluate the audio acquisition quality of the teaching equipment of the online education system. The specific expression is: ; The maximum amplitude difference of the p-th segment of collected audio is an important attribute of the audio signal. It reflects the strength of the sound. The maximum amplitude difference is an indicator to measure the dynamic range of the audio signal in this segment. If the maximum amplitude difference deviates greatly from the reference maximum amplitude difference, it indicates that the collected audio may be distorted.

[0047] It is the reference maximum amplitude difference of the p-th segment of collected audio, indicating the reference value for analyzing the maximum amplitude difference of the p-th segment of collected audio, and is analyzed based on the reference time domain waveform diagram.

[0048] It is the total amplitude deviation of the p-th segment of collected audio. It is an indicator to measure the stability and consistency of the collected audio signal, and reflects the fluctuation of the amplitude of the collected audio signal.

[0049] The capture delay duration for capturing the audio for the pth segment is critical to ensuring the synchronization of the captured audio data, especially in scenarios of multi-channel audio capture and synchronization of audio and video.

[0050] e is a natural constant, p is the number of each segment of collected audio, , q is the total number of audio segments collected, It is the influence factor corresponding to the unit deviation of the maximum amplitude difference preset in the information management database, which represents the numerical value of the influence degree of the unit deviation of the maximum amplitude difference on the audio acquisition quality assessment factor. When used, the influence factor corresponding to the unit deviation of the maximum amplitude difference can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the maximum amplitude difference and the influence factor corresponding to the unit deviation of the maximum amplitude difference preset in the information management database form a mapping set, and the real-time maximum amplitude difference is input into the mapping set to obtain the influence factor corresponding to the unit deviation of the maximum amplitude difference. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0051] It is the influence factor corresponding to the unit value of the total amplitude deviation preset in the information management database, which represents the influence degree of the unit value of the total amplitude deviation on the audio acquisition quality assessment factor. When used, the influence factor corresponding to the unit value of the total amplitude deviation can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the total amplitude deviation value and the influence factor corresponding to the unit value of the total amplitude deviation preset in the information management database form a mapping set, and the real-time total amplitude deviation value is input into the mapping set to obtain the influence factor corresponding to the unit value of the total amplitude deviation. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0052] It is the impact factor corresponding to the unit value of the acquisition delay time preset in the information management database, which represents the numerical value of the influence degree of the unit value of the acquisition delay time on the audio acquisition quality evaluation factor. When used, the impact factor corresponding to the unit value of the acquisition delay time can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the acquisition delay time and the impact factor corresponding to the unit value of the acquisition delay time preset in the information management database form a mapping set, and the real-time acquisition delay time is input into the mapping set to obtain the impact factor corresponding to the unit value of the acquisition delay time. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0053] in, It is an evaluation factor of the audio acquisition quality of the teaching equipment of the online education system. Due to the existence of the delay time of the acquired audio, the audio signal may be misplaced in time during the transmission and processing process, which may cause the amplitude characteristics of the acquired audio to change. Specifically, the maximum amplitude difference of the acquired audio may deviate significantly from the maximum reference amplitude difference, that is, there is a large difference between the actual maximum value of the acquired audio amplitude and the expected or standard value. This difference may mean that the acquired audio signal has overshoot, clipping or distortion during the transmission process, thereby affecting the clarity and dynamic range of the acquired audio, and further leading to the total deviation value of the acquired audio amplitude. Increase, a large deviation value of the maximum amplitude difference may cause distortion of the collected audio. A high total amplitude deviation value indicates that there is more noise, distortion and inconsistency in the collected audio signal, which reduces the clarity and intelligibility of the collected audio. Excessive acquisition delay will destroy the synchronization of other collected audio signals. Low latency is an important prerequisite for ensuring audio quality. Therefore, the three parameters are processed comprehensively to provide a multi-dimensional perspective for the evaluation of the quality of collected audio for online teaching in the online education system. It can more scientifically evaluate audio performance and applicability, thereby making more reasonable decisions and ensuring the high-quality development of online teaching in the online education system.

[0054] Step 3: Collect the network performance parameters of the teaching network of the online education system, evaluate the network quality index of the teaching network of the online education system, and provide management feedback for the information management of the online education system.

[0055] It needs to be further explained that the above-mentioned collection of network performance parameters of the teaching network belonging to the online education system refers to the online education system receiving the network performance parameters of the teaching network, and the teaching network and the hardware (such as a computer) belonging to the online education system transmit parameters through a network connection, wherein the network performance parameters of the teaching network belonging to the online education system include the network transmission rate curve of the teaching network belonging to the online education system during the network performance detection period. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters, and the direction in which these parameters can be fed back (i.e., positive and negative relationships) is consistent.

[0056] In a specific embodiment, the present invention obtains the network quality index of the teaching network belonging to the online education system by comprehensively analyzing the data transmission volume, network transmission rate change rate deviation value and network transmission rate maximum fluctuation value of the teaching network belonging to the online education system during the network performance detection period, so as to continuously optimize and improve the network environment of online education, so as to ensure that the audio and image information on the teacher's side can be stably and efficiently transmitted to the student side, thereby fundamentally enhancing the overall quality of online teaching.

[0057] Specifically, the information management of the online education system is managed and fed back, and the specific feedback process is as follows: The network quality index of the teaching network of the online education system is compared with the network quality threshold. If the network quality index of the teaching network of the online education system is less than or equal to the network quality threshold, management feedback is given to the information management of the online education system. The above network quality threshold represents the minimum value of the reasonable range of the network quality index of the teaching network of the online education system, and the numerical value is set by the network analysis engineer. If the network quality index of the teaching network of the online education system is less than or equal to the network quality threshold, it indicates that the current teaching network is not sufficient to support high-quality teaching audio and image transmission to the student end, which may have a negative impact on the students' learning experience, and management feedback is required for the information management of the online education system. The specific feedback process is: the current teaching network is analyzed through network diagnostic software (such as a network performance monitor), and the system feeds back the network analysis log generated by the network diagnostic software to the network analysis engineer, and the network analysis engineer improves the teaching network based on the network analysis log. If the network quality index of the teaching network of the online education system is greater than the network quality threshold, it indicates that the current teaching network can support high-quality teaching audio and image transmission to the student end, and the foundation of online teaching in the online education system is well guaranteed, and there is no need for management feedback.

[0058] Specifically, the network performance parameters of the teaching network of the online education system specifically refer to the network transmission rate curve of the teaching network of the online education system within the network performance detection period; the above network performance detection period represents the time period for analyzing the quality of the teaching network of the online education system, which is formulated by the network analysis engineer according to the actual situation. In an example embodiment, the network transmission rate curve is as follows: Figure 4 As shown, it clearly shows the relationship between the network transmission rate and the network performance detection time point. The horizontal axis is the network performance detection time point in seconds, and the vertical axis is the network transmission rate in bit rate.

[0059] Specifically, the network quality index of the teaching network of the online education system is evaluated, and the specific evaluation process is as follows: The network transmission rate curve of the teaching network of the online education system within the network performance detection period is processed to obtain the network transmission rate function of the teaching network of the online education system within the network performance detection period, and the network transmission rate function is integrated to obtain the data transmission volume of the teaching network of the online education system within the network performance detection period; the network transmission rate curve of the teaching network of the online education system within the network performance detection period is processed, and the specific data processing process is as follows: first, the network transmission rate curve of the teaching network of the online education system within the network performance detection period is denoised by a median filter, and the denoised curve is input into the matrix laboratory software, and the function generation function of the matrix laboratory software is used to convert it into the network transmission rate function of the teaching network of the online education system within the network performance detection period; the data transmission volume of the teaching network of the online education system within the network performance detection period, in an example embodiment, the specific acquisition process is: the network transmission rate function of the teaching network of the online education system within the network performance detection period is integrated, that is, ,in, is the network transmission rate function of the teaching network of the online education system during the network performance detection period, such as Figure 4 As shown, is the starting time point 3 of the network performance detection cycle, The end time point 4 of the network performance detection cycle.

[0060] The network transmission rate function of the teaching network of the online education system within the network performance detection period is derived to obtain the corresponding change rate function of the network transmission rate of the teaching network of the online education system within the network performance detection period, and the corresponding change rate of the network transmission rate of each network performance detection point is located and extracted, and the standard deviation is used to obtain the corresponding change rate deviation value of the network transmission rate of the teaching network of the online education system within the network performance detection period; the above-mentioned network performance detection points are time points randomly arranged by network analysis engineers within the network performance detection period; the above-mentioned network transmission rate corresponding change rate deviation value of the teaching network of the online education system within the network performance detection period, the specific standard deviation processing process is: , is the corresponding change rate of the network transmission rate of the kth network performance detection point, is the result of the mean value processing of the network transmission rate corresponding to the change rate of each network performance detection point, k is the number of each network performance detection point, , y is the total number of network performance detection points, and the standard deviation processing result is the corresponding change rate deviation value of the network transmission rate of the teaching network of the online education system during the network performance detection cycle.

[0061] The network transmission rate curve of the teaching network of the online education system in the network performance detection period is located and extracted from the network transmission rate corresponding to the highest point of the curve and the network transmission rate corresponding to the lowest point of the curve, and the maximum fluctuation value of the network transmission rate of the teaching network of the online education system in the network performance detection period is obtained by difference processing, and the network quality index of the teaching network of the online education system is obtained by comprehensive evaluation; the above-mentioned maximum fluctuation value of the network transmission rate, in an example embodiment, Figure 4 As shown, the network transmission rate corresponding to the highest point 6 of the curve is subtracted from the network transmission rate corresponding to the lowest point 5 of the curve.

[0062] Furthermore, the network quality index of the teaching network to which the online education system belongs, in this embodiment, is obtained by comprehensively analyzing the data transmission volume, the network transmission rate corresponding to the change rate deviation value and the maximum fluctuation value of the network transmission rate of the teaching network to which the online education system belongs during the network performance detection period, and is used to evaluate the network quality of the teaching network to which the online education system belongs. The specific expression is: ; It is the data transmission volume of the teaching network of the online education system during the network performance detection period, which refers to the total data volume that can be transmitted by the teaching network of the online education system during the network performance detection period.

[0063] It is the change rate deviation value corresponding to the network transmission rate of the teaching network of the online education system during the network performance detection period. It refers to the difference between the change rate of the network transmission rate and the average change rate during the network performance detection period. It is an indicator reflecting the stability of the network transmission rate.

[0064] It is the maximum fluctuation value of the network transmission rate of the teaching network of the online education system during the network performance detection cycle. It refers to the difference between the maximum and minimum values ​​of the network transmission rate during the network performance detection cycle, reflecting the amplitude of the fluctuation of the network transmission rate.

[0065] e is a natural constant, It is the impact factor corresponding to the unit value of data transmission volume preset in the information management database, which represents the numerical value of the degree of influence of the unit value of data transmission volume on the network quality index. When used, the impact factor corresponding to the unit value of data transmission volume can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the data transmission volume and the impact factor corresponding to the unit value of data transmission volume preset in the information management database form a mapping set, and the real-time data transmission volume is input into the mapping set to obtain the impact factor corresponding to the unit value of data transmission volume. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0066] It is the influence factor of the unit deviation value of the network transmission rate corresponding to the change rate preset in the information management database, which represents the influence degree of the unit value of the change rate deviation value corresponding to the network transmission rate on the network quality index. When used, the influence factor of the unit deviation value of the network transmission rate corresponding to the change rate can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the network transmission rate corresponding to the change rate deviation value and the influence factor of the unit deviation value of the network transmission rate corresponding to the change rate preset in the information management database form a mapping set, and the real-time network transmission rate corresponding to the change rate deviation value is input into the mapping set to obtain the influence factor of the unit deviation value of the network transmission rate corresponding to the change rate. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0067] It is the impact factor corresponding to the unit value of the maximum fluctuation value of the network transmission rate preset in the information management database, which represents the degree of influence of the unit value of the maximum fluctuation value of the network transmission rate on the network quality index. When used, the impact factor corresponding to the maximum fluctuation value of the network transmission rate can be directly obtained from the information management database, and the corresponding relationship can be a preset mapping relationship. For example, the maximum fluctuation value of the network transmission rate and the impact factor corresponding to the unit value of the maximum fluctuation value of the network transmission rate preset in the information management database form a mapping set, and the real-time maximum fluctuation value of the network transmission rate is input into the mapping set to obtain the impact factor corresponding to the unit value of the maximum fluctuation value of the network transmission rate. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0068] in, It is the network quality index of the teaching network of the online education system. The transmission rate of the teaching network environment of the online education system is unstable, which will cause its change rate to accelerate, and then make the change rate of the network transmission rate show large fluctuations. This high-frequency and large-scale change directly leads to the increase of the deviation value of the network transmission rate change rate and the maximum fluctuation value of the network transmission rate. The unstable network transmission rate may also directly lead to a decrease in the data transmission volume, thereby jointly reducing the quality of the teaching network of online education, resulting in the inability to ensure the stable transmission and high-quality presentation of good image data and good audio data, which not only affects the real-time and smoothness of online teaching interaction, but also may have a negative impact on students' learning experience and understanding. The basic indicator of data transmission volume reflects the basic capabilities of the teaching network of the online education system. The deviation value of the network transmission rate change rate and the maximum fluctuation value are supplementary indicators to further refine the dynamic characteristics of the online teaching network performance. By scientifically and reasonably assigning different weights to these three parameters, a comprehensive and accurate network quality evaluation model can be constructed to provide data support for improving the quality of online teaching networks in online education systems.

[0069] In a specific embodiment, the present invention provides an information management method and system based on an online education system. By analyzing the image acquisition information of the teaching equipment belonging to the online education system, the corresponding image acquisition quality evaluation factor is obtained, thereby managing and feedbacking the image acquisition information of the teaching equipment belonging to the online education system. By analyzing the audio acquisition information of the teaching equipment belonging to the online education system, the audio acquisition quality evaluation factor of the teaching equipment belonging to the online education system is obtained, thereby managing and feedbacking the audio acquisition information of the teaching equipment belonging to the online education system. Finally, the network quality index of the teaching network belonging to the online education system is evaluated, which enhances the initiative and timeliness of teaching equipment management, helps to improve the overall performance of the online education system, and lays a solid foundation for providing a high-quality online teaching environment.

[0070] Reference Figure 2 As shown, the second aspect of the present invention provides a system for applying an information management method based on an online education system, including: an image information analysis module, an audio information analysis module and a network quality assessment module.

[0071] The second aspect of the present invention provides a system for application such as an information management method based on an online education system, which also includes an information management database, wherein the information management database is used to store the influencing factors corresponding to the unit values ​​of color deviation values, the influencing factors corresponding to the unit values ​​of the total number of pixels, the influencing factors corresponding to the unit values ​​of grayscale variance values, the first parameter set, the influencing factors corresponding to the unit deviations belonging to the maximum amplitude difference, the influencing factors corresponding to the unit values ​​of the total amplitude deviation, the influencing factors corresponding to the unit values ​​of the acquisition delay time, the second parameter set, the influencing factors corresponding to the unit values ​​of the data transmission volume, the influencing factors corresponding to the unit deviation values ​​of the change rate of the network transmission rate, and the influencing factors corresponding to the unit values ​​of the maximum fluctuation value of the network transmission rate.

[0072] The image information analysis module is connected to the audio information analysis module and the information management database respectively. The audio information analysis module is connected to the network quality evaluation module and the information management database respectively. The network quality evaluation module is connected to the information management database.

[0073] The image information analysis module is used to obtain the image acquisition information of the teaching equipment belonging to the online education system, analyze the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the image acquisition quality assessment threshold. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, the audio information analysis module is executed; if the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, the image acquisition information of the teaching equipment belonging to the online education system is managed and fed back.

[0074] The audio information analysis module is used to collect audio collection information of the teaching equipment belonging to the online education system, analyze the audio collection quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, the network quality assessment module is executed; if the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, management feedback is performed on the audio collection information of the teaching equipment belonging to the online education system.

[0075] The network quality evaluation module is used to collect network performance parameters of the teaching network belonging to the online education system, evaluate the network quality index of the teaching network belonging to the online education system, and thereby provide management feedback for the information management of the online education system.

[0076] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An information management method based on an online education system, characterized in that: include: Step 1: obtain the image acquisition information of the teaching equipment belonging to the online education system, analyze the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the image acquisition quality assessment threshold. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, execute step 2. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, manage feedback on the image acquisition information of the teaching equipment belonging to the online education system; Step 2: Collect audio collection information of the teaching equipment belonging to the online education system, analyze the audio collection quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, execute step 3. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, manage feedback on the audio collection information of the teaching equipment belonging to the online education system; Step 3: Collect the network performance parameters of the teaching network of the online education system, evaluate the network quality index of the teaching network of the online education system, and provide management feedback for the information management of the online education system.

2. The information management method based on the online education system according to claim 1, characterized in that: The image acquisition quality assessment factor of the teaching equipment of the online education system is analyzed, and the specific analysis process is as follows: Extracting image acquisition information of each acquired image of the teaching device belonging to the online education system within an image analysis period from the image acquisition information of the teaching device belonging to the online education system, locating and extracting the color value of each image data analysis point from the image acquisition information of each acquired image, obtaining the reference color value of each image data analysis point of each acquired image, and performing data processing with the color value of each image data analysis point to obtain the color deviation value of each acquired image; Extracting the number of height pixels and the number of width pixels of each collected image from the image collection information of each collected image, and performing product processing to obtain the total number of pixel points of each collected image; The grayscale value of each pixel is extracted from the image acquisition information of each acquired image, and data processing is performed with the number of height pixels and width pixels of each acquired image to obtain the grayscale variance value of each acquired image. Based on this comprehensive analysis, the image acquisition quality evaluation factor of the teaching equipment belonging to the online education system is obtained.

3. The information management method based on the online education system according to claim 1, characterized in that: The management feedback of the image acquisition information of the teaching equipment belonging to the online education system is performed, and the specific feedback process is as follows: The image acquisition information of the teaching equipment belonging to the online education system is transmitted to the first parameter set in the information management database, and a first parameter set report is generated, thereby performing management feedback on the image acquisition information of the teaching equipment belonging to the online education system.

4. The information management method based on the online education system according to claim 1, characterized in that: The analysis of the audio collection quality evaluation factor of the teaching equipment of the online education system is as follows: Extracting audio acquisition information of each segment of audio collected by the teaching equipment belonging to the online education system within an audio analysis period from the audio acquisition information of the teaching equipment belonging to the online education system, extracting a time domain waveform of each segment of audio collected from the audio acquisition information of each segment of audio collected, extracting a maximum amplitude and a minimum amplitude of each segment of audio collected from the time domain waveform of each segment of audio collected, performing difference processing to obtain a maximum amplitude difference of each segment of audio collected, obtaining a reference time domain waveform of each segment of audio collected, and analyzing to obtain a reference maximum amplitude difference of each segment of audio collected; Locate and extract the amplitude of each audio analysis data point from the time domain waveform of each segment of collected audio, extract the reference amplitude of each audio analysis data point from the reference time domain waveform of each segment of collected audio, perform data processing with the amplitude of each audio analysis data point, and obtain the total amplitude deviation value of each segment of collected audio; The collection start time point of each segment of collected audio is extracted from the audio collection information of each segment of collected audio, and the reference start time point of each segment of collected audio is obtained. The reference start time point of each segment of collected audio is then subtracted from the corresponding collection start time point of each segment of collected audio to obtain the collection delay duration of each segment of collected audio. Based on this comprehensive analysis, the audio collection quality evaluation factor of the teaching equipment belonging to the online education system is obtained.

5. The information management method based on the online education system according to claim 1, characterized in that: The management feedback of the audio collection information of the teaching equipment belonging to the online education system is performed, and the specific feedback process is as follows: The audio collection information of the teaching equipment belonging to the online education system is transmitted to the second parameter set in the information management database, and a second parameter set report is generated, thereby performing management feedback on the audio collection information of the teaching equipment belonging to the online education system.

6. The information management method based on the online education system according to claim 1, characterized in that: The network performance parameters of the teaching network to which the online education system belongs specifically refer to the network transmission rate curve of the teaching network to which the online education system belongs during the network performance detection period.

7. The information management method based on the online education system according to claim 6 is characterized in that: The network quality index of the teaching network of the online education system is evaluated, and the specific evaluation process is as follows: The network transmission rate curve of the teaching network of the online education system in the network performance detection period is processed to obtain the network transmission rate function of the teaching network of the online education system in the network performance detection period, and the network transmission rate function is processed to obtain the data transmission volume of the teaching network of the online education system in the network performance detection period; The network transmission rate function of the teaching network of the online education system within the network performance detection period is derived to obtain the corresponding change rate function of the network transmission rate of the teaching network of the online education system within the network performance detection period, and the corresponding change rate of the network transmission rate of each network performance detection point is located and extracted, and the standard deviation is processed to obtain the corresponding change rate deviation value of the network transmission rate of the teaching network of the online education system within the network performance detection period; The network transmission rate curve of the teaching network of the online education system during the network performance detection period is located and extracted from the network transmission rate corresponding to the highest point of the curve and the network transmission rate corresponding to the lowest point of the curve. The maximum fluctuation value of the network transmission rate of the teaching network of the online education system during the network performance detection period is obtained through difference processing. The network quality index of the teaching network of the online education system is obtained through comprehensive evaluation.

8. The information management method based on the online education system according to claim 7, characterized in that: The network quality index of the teaching network to which the online education system belongs is specifically expressed as: ； in, is the network quality index of the teaching network of the online education system, It is the data transmission volume of the teaching network of the online education system during the network performance detection period. is the change rate deviation value of the network transmission rate of the teaching network of the online education system during the network performance detection period, is the maximum fluctuation value of the network transmission rate of the teaching network of the online education system during the network performance detection period, e is a natural constant, is the impact factor corresponding to the unit value of data transmission volume preset in the information management database, It is the influencing factor of the unit deviation value of the network transmission rate corresponding to the change rate preset in the information management database. It is the impact factor corresponding to the unit value of the maximum fluctuation value of the network transmission rate preset in the information management database.

9. The information management method based on the online education system according to claim 1, characterized in that: The management feedback of the information management of the online education system is as follows: The network quality index of the teaching network to which the online education system belongs is compared with the network quality threshold. If the network quality index of the teaching network to which the online education system belongs is less than or equal to the network quality threshold, management feedback is provided to the information management of the online education system.

10. A system using the information management method based on an online education system as claimed in any one of claims 1 to 9, characterized in that: include: An image information analysis module is used to obtain image acquisition information of the teaching equipment belonging to the online education system, analyze the image acquisition quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the image acquisition quality assessment threshold. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is greater than the image acquisition quality assessment threshold, the audio information analysis module is executed. If the image acquisition quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the image acquisition quality assessment threshold, management feedback is performed on the image acquisition information of the teaching equipment belonging to the online education system. The audio information analysis module is used to collect audio collection information of the teaching equipment belonging to the online education system, analyze the audio collection quality assessment factor of the teaching equipment belonging to the online education system, and compare it with the audio collection quality assessment threshold. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is greater than the audio collection quality assessment threshold, the network quality assessment module is executed. If the audio collection quality assessment factor of the teaching equipment belonging to the online education system is less than or equal to the audio collection quality assessment threshold, the audio collection information of the teaching equipment belonging to the online education system is managed and fed back; The network quality assessment module is used to collect the network performance parameters of the teaching network of the online education system, evaluate the network quality index of the teaching network of the online education system, and thus provide management feedback for the information management of the online education system.

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