Mobile learning method and system based on 5g mobile network

Abstract

The application provides a mobile learning method and system based on a 5G mobile network, which performs screen recording on the online learning process of a student, thereby obtaining corresponding online learning records, and obtains online learning average progress information, mobile network traffic usage information and learning time usage information of the student according to the online learning records, determines the mobile network traffic peak information corresponding to the online learning process of the student according to the above information, and finally adjusts the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information, so that the student can be allocated a personalized traffic channel according to the actual online learning data interaction demand of the student, thereby improving the data transmission efficiency of the 5G mobile network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent education, and particularly relates to a mobile learning method and system based on a 5G mobile network. BACKGROUND

[0002] At present, online learning methods have been widely used in the field of intelligent education. Online learning methods can liberate students from fixed learning time and fixed learning places, so that students can learn at appropriate time and place according to their actual needs, which greatly improves the flexibility of student learning. Online learning is achieved by means of mobile network to realize data interaction. In order to maximize the data interaction flow field of online learning, 5G mobile network has become the first choice of online learning data network. Although 5G mobile network has significant improvement in network speed and the like, when providing corresponding online teaching network for students, it is according to the established traffic channel to carry out data interaction, and it cannot allocate personalized traffic channel for students according to the actual online learning data interaction needs of students, which is not conducive to improving the data transmission efficiency of 5G mobile network. It can be seen that the prior art needs a method capable of adjusting the traffic channel allocation state of 5G mobile network according to the actual online learning data interaction needs of students. SUMMARY

[0003] In view of the defects in the prior art, the present application provides a mobile learning method and system based on a 5G mobile network. The online learning process of a student is recorded to obtain the online learning record of the student, and the online learning average progress information of the student is determined according to the online learning record. The mobile network traffic usage information corresponding to the online learning process of the student is determined according to the online learning average progress information. The learning time usage information of the student in the online learning process is obtained, and the mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the learning time usage information and the mobile network traffic usage information. The traffic channel allocation state of the 5G mobile network to the student is adjusted according to the mobile network traffic peak value information. It can be seen that the mobile learning method and system based on the 5G mobile network can record the online learning process of the student to obtain the corresponding online learning record, and obtain the online learning average progress information, the mobile network traffic usage information and the learning time usage information of the student according to the online learning record. The mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the above information, and finally the traffic channel allocation state of the 5G mobile network to the student is adjusted according to the mobile network traffic peak value information. This can allocate personalized traffic channel for students according to the actual online learning data interaction needs of students, thereby improving the data transmission efficiency of 5G mobile network.

[0004] The application provides a mobile learning method based on a 5G mobile network, characterized in that it comprises the following steps:

[0005] Step S1: screen recording is performed on the online learning process of a student to obtain the online learning record of the student, and the average progress information of the online learning of the student is determined according to the online learning record;

[0006] Step S2: the mobile network traffic usage information corresponding to the online learning process of the student is determined according to the average progress information of the online learning;

[0007] Step S3: the learning time usage information of the student in the online learning process is obtained, the peak value information of the mobile network traffic corresponding to the online learning process of the student is determined according to the learning time usage information and the mobile network traffic usage information, and the traffic channel allocation state of the 5G mobile network to the student is adjusted according to the peak value information of the mobile network traffic;

[0008] Further, in the step S1, the screen recording is performed on the online learning process of a student to obtain the online learning record of the student, and the average progress information of the online learning of the student is determined according to the online learning record, which specifically comprises:

[0009] Step S101: screen recording is performed on the online learning process of a student to obtain the online learning image record of the student;

[0010] Step S102: the online learning image record is identified to obtain the actual course learning cumulative time of the student in a plurality of course learning processes in one day, and the online learning progress value corresponding to each course learning of the student is determined according to the actual course learning cumulative time and the total course learning time of each course learning;

[0011] Step S103: the average progress difference value of the online learning of the student is determined according to the following formula (1):

[0012]

[0013] In the above formula (1), Arv(s n ,s n-1 ) represents the average progress difference value of the online learning between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, N represents the total number of course learning of the student, sub(s n ,s n-1 ) represents the online learning progress value s na difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student n-1 a difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student n a difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student n-1 a difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student

[0014] Further, in the step S2, the mobile network traffic usage information corresponding to the online learning process of the student is determined according to the online learning average progress information, and specifically includes:

[0015] The mobile network traffic average usage value Net(s n ,s n-1 ) corresponding to the n-th course learning and the n-1-th course learning of the student is determined according to a difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student, and the following formula (2):

[0016]

[0017] In the above formula (2), Arv(s n ,s n-1 ) represents a difference between the online learning progress value s corresponding to the n-th course learning of the student and the online learning progress value s corresponding to the n-1-th course learning of the student, N represents the total number of course learning performed by the student, and δ represents the total number of bytes corresponding to the online learning average progress difference Arv(s n ,s n-1 );

[0018] Further, in the step S3, the learning time usage information of the student in the online learning process is obtained, and the mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the learning time usage information and the mobile network traffic usage information, and then the mobile network traffic peak value information is used to adjust the traffic channel allocation state of the 5G mobile network to the student, and specifically includes:

[0019] In step S301, the online learning video record is identified to obtain the longest continuous learning time value of the student in all course learning within one day;

[0020] In step S302, the maximum mobile network traffic peak value L corresponding to the online learning process of the student is determined according to the longest continuous learning time value, the average mobile network traffic usage value, and the following formula (3):

[0021]

[0022] In the above formula (3), Net(s n ,s n-1 ) represents the average mobile network traffic usage value corresponding to the nth course learning and the (n-1)th course learning of the student, Arv(s n ,s n-1 ) represents the average online learning progress difference value between the online learning progress value corresponding to the nth course learning and the online learning progress value corresponding to the (n-1)th course learning of the student, n is 2, 3, …, N, N represents the total number of course learning of the student, and T represents the longest continuous learning time value corresponding to all course learning of the student in one day.

[0023] In step S303, the maximum mobile network traffic peak value L is compared with the preset mobile network traffic threshold value, if the maximum mobile network traffic peak value L is greater than or equal to the preset mobile network traffic threshold value, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student is increased, otherwise, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student is kept unchanged.

[0024] The application also provides a mobile learning system based on a 5G mobile network, which is characterized by comprising an online learning record module, an online learning average progress determination module, a mobile network traffic usage information determination module, and a 5G mobile network adjustment module.

[0025] The online learning record module is used for recording the online learning process of a student to obtain the online learning record of the student.

[0026] The online learning average progress determination module is used for determining the online learning average progress information of the student according to the online learning record.

[0027] The mobile network traffic usage information determination module is used for determining the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information.

[0028] The 5G mobile network adjustment module is configured to acquire learning time usage information of the student in the online learning process, determine mobile network traffic peak information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjust a traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information.

[0029] Further, the online learning record module performs screen recording on the online learning process of the student to obtain the online learning record of the student, which specifically includes:

[0030] The online learning record module performs screen recording on the online learning process of the student to obtain the online learning record of the student, which specifically includes:

[0031] And,

[0032] The online learning average progress determination module determines the online learning average progress information of the student according to the online learning record, which specifically includes:

[0033] The online learning average progress determination module determines the online learning average progress information of the student according to the online learning record, which specifically includes:

[0034] The online learning average progress determination module determines the online learning average progress information of the student according to the online learning record, which specifically includes:

[0035]

[0036] In the above formula (1), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value s n of the student in the nth course learning and the online learning progress value s n-1 in the (n-1)th course learning, N represents the total number of course learnings of the student, sub(s n ,s n-1 ) represents the difference between the online learning progress value s n of the student in the nth course learning and the online learning progress value s n-1is equal to the ratio of the actual course learning cumulative time of the student in the n-1th course learning to the total course learning time, s1 represents the online learning progress value corresponding to the first course learning of the student, and s1 is equal to the ratio of the actual course learning cumulative time of the student in the first course learning to the total course learning time;

[0037] Further, the mobile network traffic usage information determination module determines the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information, and the specific process includes:

[0038] According to the online learning average progress difference value between the online learning progress value corresponding to the n th course learning of the student and the online learning progress value corresponding to the n-1th course learning, and the following formula (2), the mobile network traffic average usage value Net(s n ,s n-1 ) corresponding to the n th course learning and the n-1th course learning of the student is determined:

[0039]

[0040] In the above formula (2), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value corresponding to the n th course learning of the student and the online learning progress value corresponding to the n-1th course learning, N represents the total number of course learning performed by the student, and δ represents the total byte number corresponding to the online learning average progress difference value Arv(s n ,s n-1 );

[0041] Further, the 5G mobile network adjustment module acquires the learning time usage information of the student in the online learning process, and determines the mobile network traffic peak value information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusts the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information, and the specific process includes:

[0042] The online learning video record is identified and processed, so as to obtain the longest continuous learning time value of the student in all course learning within one day;

[0043] And according to the longest continuous learning time value, the mobile network traffic average usage value and the following formula (3), the mobile network traffic highest peak value L corresponding to the online learning process of the student is determined:

[0044]

[0045] In the above formula (3), Net(s n ,s n-1 ) represents the average mobile network traffic usage value of the student corresponding to the nth course learning and the (n-1)th course learning, Arv(s n ,s n-1 ) represents the average online learning progress difference value between the online learning progress value corresponding to the nth course learning and the online learning progress value corresponding to the (n-1)th course learning of the student, n is 2, 3, …, N, N represents the total number of course learning of the student, and T represents the longest continuous learning time value of the student in all corresponding course learning within one day.

[0046] The highest peak value L of the mobile network traffic is compared with the preset mobile network traffic threshold value. If the highest peak value L of the mobile network traffic is greater than or equal to the preset mobile network traffic threshold value, the bandwidth or network speed of the traffic channel allocated to the student by the 5G mobile network is increased, otherwise, the bandwidth or network speed of the traffic channel allocated to the student by the 5G mobile network remains unchanged.

[0047] Compared with the prior art, the mobile learning method and system based on the 5G mobile network can record the online learning process of the student to obtain the online learning record of the student, determine the average online learning progress information of the student according to the online learning record, determine the mobile network traffic usage information corresponding to the online learning process of the student according to the average online learning progress information, obtain the learning time usage information of the student in the online learning process, determine the mobile network traffic peak value information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjust the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information. It can be seen that the mobile learning method and system based on the 5G mobile network can record the online learning process of the student to obtain the corresponding online learning record, obtain the average online learning progress information, the mobile network traffic usage information and the learning time usage information of the student according to the online learning record, determine the mobile network traffic peak value information corresponding to the online learning process of the student according to the above information, and finally adjust the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information. This can allocate personalized traffic channels to students according to their actual online learning data interaction requirements, thereby improving the data transmission efficiency of the 5G mobile network.

[0048] Other features and advantages of the present application will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0049] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0051] Figure 1 The flowchart of the mobile learning method based on the 5G mobile network provided by the present application.

[0052] Figure 2 The structural diagram of the mobile learning system based on the 5G mobile network provided by the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with the help of the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0054] Reference Figure 1 The flowchart of the mobile learning method based on the 5G mobile network provided by the present application. The mobile learning method based on the 5G mobile network comprises the following steps:

[0055] Step S1, screen recording operation is performed on the online learning process of a student, so as to obtain the online learning record of the student, and the online learning average progress information of the student is determined according to the online learning record;

[0056] Step S2, the mobile network traffic usage information corresponding to the online learning process of the student is determined according to the online learning average progress information;

[0057] Step S3, obtaining the learning time usage information of the student in the online learning process, and determining the mobile network traffic peak information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusting the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information.

[0058] The beneficial effects of the above technical solutions are that the mobile learning method based on the 5G mobile network can perform screen recording operation on the online learning process of the student, thereby obtaining the corresponding online learning record, and according to the online learning record, obtaining the online learning average progress information, the mobile network traffic usage information and the learning time usage information of the student, and then determining the mobile network traffic peak information corresponding to the online learning process of the student according to the above information, and finally adjusting the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information, so as to allocate personalized traffic channels to the student according to the actual online learning data interaction demand of the student, thereby improving the data transmission efficiency of the 5G mobile network.

[0059] Preferably, in the step S1, the online learning record of the student is obtained by performing screen recording operation on the online learning process of the student, and the online learning average progress information of the student is determined according to the online learning record, specifically including:

[0060] Step S101, performing screen recording operation on the online learning process of the student, thereby obtaining the online learning image record of the student;

[0061] Step S102, identifying the online learning image record, thereby obtaining the actual course learning cumulative time of the student in a plurality of course learning in a day, and determining the online learning progress value corresponding to each course learning of the student according to the actual course learning cumulative time and the total course learning time of each course learning;

[0062] Step S103, determining the online learning average progress difference value of the student according to the following formula (1):

[0063]

[0064] In the above formula (1), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, N represents the total number of course learning performed by the student, sub(s n ,s n-1 ) represents the online learning progress value sn the online learning progress value s corresponding to the n-th course learning of the student n-1 the online learning progress value s corresponding to the n-th course learning of the student n the online learning progress value s corresponding to the n-th course learning of the student n-1 the online learning progress value s corresponding to the n-th course learning of the student n s1 represents the online learning progress value corresponding to the first course learning of the student, and s1 is equal to the ratio of the actual course learning cumulative time of the first course learning of the student to the total course learning time.

[0065] The beneficial effects of the above technical solutions are that the online learning process of the student is recorded through the screen recording operation, the online learning process of the student is recorded in all directions, the online learning progress value of the student is accurately determined, the online learning average progress difference value of the student is effectively determined through the above formula (1), the average difference of the online learning progress of the student between different course learnings is effectively determined, and the correctness of the subsequent calculation of the mobile network traffic usage information is facilitated.

[0066] Preferably, in the step S2, the mobile network traffic usage information corresponding to the online learning process of the student is determined according to the online learning average progress information, and specifically includes:

[0067] According to the online learning average progress difference value between the online learning progress value corresponding to the n-th course learning of the student and the online learning progress value corresponding to the n-1-th course learning, and the following formula (2), the average mobile network traffic usage value Net(s n ,s n-1 ) corresponding to the n-th course learning and the n-1-th course learning of the student is determined.

[0068]

[0069] In the above formula (2), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value corresponding to the n-th course learning of the student and the online learning progress value corresponding to the n-1-th course learning, N represents the total number of course learnings performed by the student, and δ represents the total number of bytes corresponding to the online learning average progress difference value Arv(s n ,s n-1 ).

[0070] The beneficial effects of the above technical solutions are: the mobile network traffic average use value of the student in the online learning process is calculated by the formula (2), which can effectively determine the corresponding mobile network traffic average use value according to the actual online learning progress of the student, so as to truly reflect the data interaction demand of the student in the online learning process.

[0071] Preferably, in the step S3, the learning time use information of the student in the online learning process is obtained, and the mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the learning time use information and the mobile network traffic use information, and the traffic channel allocation state of the 5G mobile network to the student is adjusted according to the mobile network traffic peak value information, specifically including:

[0072] In step S301, the online learning video record is identified to obtain the longest continuous learning time value of the student in all course learning within one day;

[0073] In step S302, the longest continuous learning time value, the mobile network traffic average use value and the following formula (3) are used to determine the mobile network traffic peak value L corresponding to the online learning process of the student:

[0074]

[0075] In the above formula (3), Net(s n ,s n-1 ) represents the mobile network traffic average use value corresponding to the nth course learning and the (n-1)th course learning of the student, Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value corresponding to the nth course learning and the online learning progress value corresponding to the (n-1)th course learning of the student, n is 2, 3, …, N, N represents the total number of course learning of the student, and T represents the longest continuous learning time value of the student in all course learning within one day;

[0076] In step S303, the mobile network traffic peak value L is compared with the preset mobile network traffic threshold value, if the mobile network traffic peak value L is greater than or equal to the preset mobile network traffic threshold value, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student is increased, otherwise, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student is kept unchanged.

[0077] The beneficial effects of the above technical solutions are: the peak value of the mobile network traffic corresponding to the online learning process of the student is calculated by the formula (3), the maximum mobile network traffic demand of the student in the online learning process can be accurately determined, so that the student can be allocated a personalized traffic channel in subsequent targeted manner, thereby improving the data transmission efficiency of the 5G mobile network.

[0078] Referring to Figure 2 A structure schematic diagram of a mobile learning system based on a 5G mobile network is provided for an embodiment of the present application. The mobile learning system based on the 5G mobile network comprises an online learning recording module, an online learning average progress determination module, a mobile network traffic usage information determination module and a 5G mobile network adjustment module; wherein,

[0079] The online learning recording module is used for recording the online learning process of the student by screen recording, so as to obtain the online learning record of the student;

[0080] The online learning average progress determination module is used for determining the online learning average progress information of the student according to the online learning record;

[0081] The mobile network traffic usage information determination module is used for determining the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information;

[0082] The 5G mobile network adjustment module is used for obtaining the learning time usage information of the student in the online learning process, and determining the mobile network traffic peak value information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusting the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information.

[0083] The beneficial effects of the above technical solutions are: the mobile learning method based on the 5G mobile network can record the online learning process of the student by screen recording, so as to obtain the corresponding online learning record, and obtain the online learning average progress information, the mobile network traffic usage information and the learning time usage information of the student according to the online learning record, then determine the mobile network traffic peak value information corresponding to the online learning process of the student according to the above information, and finally adjust the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information, so that the personalized traffic channel can be allocated to the student according to the actual online learning data interaction demand of the student, thereby improving the data transmission efficiency of the 5G mobile network.

[0084] Preferably, the online learning record module records the online learning process of the student by screen recording, so as to obtain the online learning record of the student, specifically comprising:

[0085] screen recording of the student's online learning process to obtain the online learning video record of the student;

[0086] and,

[0087] The online learning average progress determination module determines the online learning average progress information of the student according to the online learning record, and the determination specifically includes:

[0088] The online learning video record is identified to obtain the actual course learning cumulative time of the student in a plurality of course learning processes in a day, and then the actual course learning cumulative time and the total course learning time are used to determine the online learning progress value of the student in each course learning process;

[0089] The online learning average progress difference value of the student is determined according to the following formula (1):

[0090]

[0091] In the above formula (1), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value of the student in the nth course learning process and the online learning progress value of the student in the (n-1)th course learning process, N represents the total number of course learning processes of the student, sub(s n ,s n-1 ) represents the difference between the online learning progress value s n of the student in the nth course learning process and the online learning progress value s n-1 of the student in the (n-1)th course learning process, the online learning progress value s n of the student in the nth course learning process is equal to the ratio of the actual course learning cumulative time of the student in the nth course learning process to the total course learning time, and the online learning progress value s n-1 of the student in the (n-1)th course learning process is equal to the ratio of the actual course learning cumulative time of the student in the (n-1)th course learning process to the total course learning time, and s1 represents the online learning progress value of the student in the first course learning process, which is equal to the ratio of the actual course learning cumulative time of the student in the first course learning process to the total course learning time.

[0092] The beneficial effects of the above technical solutions are: by recording the online learning process of the student through screen recording operation, the online learning process of the student can be recorded in all directions, so as to ensure that the online learning progress value of the student is accurately determined, and the online learning average progress difference value of the student is calculated by the above formula (1), which can effectively determine the average difference of the online learning progress of the student between different course learning, so as to facilitate the correctness of subsequent calculation of mobile network traffic usage information.

[0093] Preferably, the mobile network traffic usage information determination module determines the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information, specifically including:

[0094] According to the online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, and the following formula (2), the mobile network traffic average usage value Net(s n ,s n-1 ) corresponding to the nth course learning and the (n-1)th course learning of the student is determined.

[0095]

[0096] In the above formula (2), Arv(s n ,s n-1 ) represents the online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, N represents the total number of course learning of the student, and δ represents the total byte number corresponding to the online learning average progress difference value Arv(s n ,s n-1 ).

[0097] The beneficial effects of the above technical solutions are: by calculating the mobile network traffic average usage value of the student in the online learning process by the above formula (2), the corresponding mobile network traffic average usage value can be effectively determined according to the actual online learning progress of the student, so as to truly reflect the data interaction demand of the student in the online learning process.

[0098] Preferably, the 5G mobile network adjustment module acquires the learning time usage information of the student in the online learning process, and determines the mobile network traffic peak value information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusts the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak value information, specifically including:

[0099] The online learning video record is identified to obtain a longest continuous learning time value of the student in all courses in a day;

[0100] The longest continuous learning time value, the average mobile network traffic value, and the following formula (3) are used to determine a highest peak value L of mobile network traffic corresponding to the online learning process of the student:

[0101]

[0102] In the above formula (3), Net(s n ,s n-1 ) represents the average mobile network traffic value corresponding to the nth course and the (n-1)th course of the student, Arv(s n ,s n-1 ) represents an average online learning progress difference between the online learning progress value corresponding to the nth course and the online learning progress value corresponding to the (n-1)th course of the student, n is 2, 3, …, N, N represents the total number of courses of the student, and T represents the longest continuous learning time value of the student in all courses in a day;

[0103] The highest peak value L of mobile network traffic is compared with a preset mobile network traffic threshold. If the highest peak value L of mobile network traffic is greater than or equal to the preset mobile network traffic threshold, the bandwidth or speed of the traffic channel allocated to the student by the 5G mobile network is increased. Otherwise, the bandwidth or speed of the traffic channel allocated to the student by the 5G mobile network is kept unchanged.

[0104] The technical scheme has the beneficial effects that the highest peak value L of mobile network traffic corresponding to the online learning process of the student is calculated by the above formula (3), the maximum mobile network traffic demand of the student in the online learning process is accurately determined, the student can be allocated a personalized traffic channel in the subsequent process, and the data transmission efficiency of the 5G mobile network is improved.

[0105] From the content of the above embodiment, the mobile learning method and system based on 5G mobile network can record the online learning process of the student to obtain the online learning record of the student, determine the average progress information of the online learning of the student according to the online learning record, determine the mobile network traffic usage information corresponding to the online learning process of the student according to the average progress information of the online learning, obtain the learning time usage information of the student in the online learning process, determine the mobile network traffic peak information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjust the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information. It can be seen that the mobile learning method and system based on 5G mobile network can record the online learning process of the student to obtain the corresponding online learning record, obtain the average progress information of the online learning of the student, the mobile network traffic usage information and the learning time usage information according to the online learning record, determine the mobile network traffic peak information corresponding to the online learning process of the student according to the above information, and finally adjust the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information. This can allocate personalized traffic channels for the student according to the actual online learning data interaction requirements of the student, thereby improving the data transmission efficiency of the 5G mobile network.

[0106] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A mobile learning method based on a 5G mobile network, characterized by, It comprises the following steps: Step S1, screen recording operation is performed on the online learning process of the student, so as to obtain the online learning record of the student, and the average progress information of the online learning of the student is determined according to the online learning record; Step S2, according to the average progress information of the online learning, the mobile network traffic usage information corresponding to the online learning process of the student is determined; Step S3, the learning time usage information of the student in the online learning process is obtained, and the mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the learning time usage information and the mobile network traffic usage information, and then the traffic channel allocation state of the 5G mobile network to the student is adjusted according to the mobile network traffic peak value information; In the step S1, the screen recording operation is performed on the online learning process of the student, so as to obtain the online learning record of the student, and the average progress information of the online learning of the student is determined according to the online learning record, which specifically comprises: Step S101, screen recording operation is performed on the online learning process of the student, so as to obtain the online learning image record of the student; Step S102, the online learning image record is identified, so as to obtain the actual course learning cumulative time of the student in a day time in a plurality of course learning, and then the online learning progress value of the student in each course learning is determined according to the actual course learning cumulative time and the total course learning time of each course learning; Step S103, the average progress difference value of the online learning of the student is determined according to the following formula (1): （1） In the above formula (1), represents the online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, and N represents the total number of course learning performed by the student, represents the online learning progress value corresponding to the nth course learning of the student n and the online learning progress value corresponding to the (n-1)th course learning n-1 , the online learning progress value corresponding to the nth course learning of the student n is equal to the ratio of the actual course learning cumulative time of the student in the nth course learning to the preset total course learning time, the online learning progress value corresponding to the (n-1)th course learning of the student n-1 is equal to the ratio of the actual course learning cumulative time of the student in the (n-1)th course learning to the preset total course learning time, s1 represents the online learning progress value corresponding to the first course learning of the student, and s1 is equal to the ratio of the actual course learning cumulative time of the student in the first course learning to the preset total course learning time.

2. The mobile learning method based on 5G mobile network of claim 1, characterized in that: In the step S2, the mobile network traffic usage information corresponding to the online learning process of the student is determined according to the average progress information of the online learning, which specifically comprises: According to the online learning average progress difference value between the corresponding online learning progress value of the student in the nth course learning and the corresponding online learning progress value in the n-1th course learning and the following formula (2), the mobile network traffic average use value of the student in the nth course learning and the n-1th course learning is determined : （2） In the above equation (2), represents an online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, and N represents the total number of course learnings performed by the student, represents the online learning average progress difference value corresponding to the total number of bytes.

3. The mobile learning method based on 5G mobile network of claim 2, characterized in that: In the step S3, the learning time usage information of the student in the online learning process is obtained, and the mobile network traffic peak value information corresponding to the online learning process of the student is determined according to the learning time usage information and the mobile network traffic usage information, and then the traffic channel allocation state of the 5G mobile network to the student is adjusted according to the mobile network traffic peak value information, which specifically comprises: Step S301, the online learning image record is identified, so as to obtain the longest continuous learning time value of the student in all course learning in a day time; Step S302, the mobile network traffic highest peak value L corresponding to the online learning process of the student is determined according to the longest continuous learning time value, the mobile network traffic average usage value and the following formula (3): （3） In the above formula (3), denotes the average use value of the mobile network traffic corresponding to the nth course learning and the (n-1)th course learning of the student, denotes the average progress difference value of online learning between the online learning progress value corresponding to the nth course learning and the online learning progress value corresponding to the (n-1)th course learning of the student, n is 2, 3, …, N, N represents the total number of course learning of the student, and T represents the longest continuous learning time value of the student in all corresponding course learning within one day. Step S303, comparing the mobile network traffic peak L with the preset mobile network traffic threshold, if the mobile network traffic peak L is greater than or equal to the preset mobile network traffic threshold, increasing the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student, otherwise, keeping the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student unchanged.

4. A mobile learning system based on a 5G mobile network, characterized by, It comprises an online learning record module, an online learning average progress determination module, a mobile network traffic usage information determination module and a 5G mobile network adjustment module; wherein, The online learning record module is used for screen recording operation on the online learning process of the student, so as to obtain the online learning record of the student; The online learning average progress determination module is used for determining the online learning average progress information of the student according to the online learning record; The mobile network traffic usage information determination module is used for determining the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information; The 5G mobile network adjustment module is used for obtaining the learning time usage information of the student in the online learning process, and determining the mobile network traffic peak information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusting the traffic channel allocation state of the 5G mobile network to the student according to the mobile network traffic peak information; The online learning record module performs screen recording operation on the online learning process of the student to obtain the online learning record of the student, which specifically comprises: Screen recording operation is performed on the online learning process of the student to obtain the online learning video record of the student; And, The online learning average progress determination module determines the online learning average progress information of the student according to the online learning record, which specifically comprises: Identifying the online learning video record to obtain the actual course learning cumulative time of the student in a plurality of course learning processes in a day, and determining the online learning progress value of the student in each course learning according to the actual course learning cumulative time of each course learning and the total course learning time; Then, the online learning average progress difference value of the student is determined according to the following formula (1): （1） In the above formula (1), represents the online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, and N represents the total number of course learning performed by the student, represents the online learning progress value s n corresponding to the nth course learning of the student minus the online learning progress value s n-1 corresponding to the (n-1)th course learning of the student, the online learning progress value s n corresponding to the nth course learning of the student is equal to the ratio of the actual course learning cumulative time of the student in the nth course learning to the total course learning time, and the online learning progress value s n-1 corresponding to the (n-1)th course learning of the student is equal to the ratio of the actual course learning cumulative time of the student in the (n-1)th course learning to the total course learning time, and s1 represents the online learning progress value corresponding to the 1st course learning of the student, and s1 is equal to the ratio of the actual course learning cumulative time of the student in the 1st course learning to the total course learning time.

5. The mobile learning system based on 5G mobile network of claim 4, wherein: The mobile network traffic usage information determination module determines the mobile network traffic usage information corresponding to the online learning process of the student according to the online learning average progress information, which specifically comprises: According to the online learning average progress difference value between the corresponding online learning progress value of the student in the nth course learning and the corresponding online learning progress value in the n-1th course learning and the following formula (2), the mobile network traffic average use value of the student in the nth course learning and the n-1th course learning is determined : （2） In the above formula (2), represents an online learning average progress difference value between the online learning progress value corresponding to the nth course learning of the student and the online learning progress value corresponding to the (n-1)th course learning, and N represents the total number of course learnings performed by the student, represents the online learning average progress difference value corresponds to the total number of bytes.

6. The mobile learning system based on 5G mobile network of claim 5, wherein: The 5G mobile network adjustment module acquires learning time usage information in the online learning process of the student, and determines mobile network traffic peak information corresponding to the online learning process of the student according to the learning time usage information and the mobile network traffic usage information, and adjusts the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student according to the mobile network traffic peak information. The longest continuous learning time value of the student in all course learning within a day is obtained by identifying and processing the online learning video record; The longest continuous learning time value, the average mobile network traffic usage value, and the following formula (3) are used to determine the highest mobile network traffic peak L corresponding to the online learning process of the student: （3） In the above formula (3), denotes the average use value of the mobile network traffic corresponding to the nth course learning and the (n-1)th course learning of the student, denotes the average progress difference value of online learning between the online learning progress value corresponding to the nth course learning and the online learning progress value corresponding to the (n-1)th course learning of the student, n is 2, 3, …, N, N represents the total number of course learning of the student, and T represents the longest continuous learning time value of the student in all corresponding course learning within one day. The highest mobile network traffic peak L is compared with the preset mobile network traffic threshold value. If the highest mobile network traffic peak L is greater than or equal to the preset mobile network traffic threshold value, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student is increased, otherwise, the traffic channel bandwidth or network speed allocated by the 5G mobile network to the student remains unchanged.

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