CN114595931A

CN114595931A - Online education information processing method using big data and readable storage medium

Info

Publication number: CN114595931A
Application number: CN202210069284.4A
Authority: CN
Inventors: 朱洪东
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-04-22
Filing date: 2021-04-22
Publication date: 2022-06-07
Also published as: CN113222357A; CN113222357B; CN114595932A

Abstract

The embodiment of the application discloses an online education information processing method and a readable storage medium applying big data, wherein the method comprises the following steps: aiming at each current teacher resource state information in the current teaching resource data set, respectively correcting the bidirectional teaching credit value of each first resource allocation strategy contained in a set of resource allocation strategies corresponding to the current teacher resource state information to obtain the corrected bidirectional teaching credit value of each first resource allocation strategy; and sequencing the first resource allocation strategies contained in a group of resource allocation strategies corresponding to the current teacher resource state information according to the corrected bidirectional teaching score values of the first resource allocation strategies, wherein the sequencing numbers of the first resource allocation strategies are positively correlated with the corrected bidirectional teaching score values.

Description

Online education information processing method using big data and readable storage medium

The application is a divisional application with the application number of "202110432908. X", the application date of "22/04/2021", and the application name of "big data resource allocation method and readable storage medium for online education".

Technical Field

The present application relates to the field of online education and big data processing technologies, and in particular, to an online education information processing method and a readable storage medium using big data.

Background

With the rapid development of information technology, especially from the internet to the mobile internet, a cross-space-time living, working and learning mode is created, so that the knowledge acquisition mode is fundamentally changed, teaching and learning can not be limited by time, space and place conditions, and the knowledge acquisition channel is flexible and diversified. In the era background of big data and cloud computing, online education (e-Learning) comes and comes, and the online education is a teaching mode taking a network as a medium. Through the internet, the students and the teachers can also carry out teaching activities even if the students and the teachers are separated by ten thousand miles.

At present, with the function of the intelligent device being more and more perfect, the interaction under the online education scene becomes more and more diversified, but at the same time, a plurality of challenges are brought to the online education server. For example, as the number of the connected online education intelligent devices is increased, the data information processing pressure of the online education server is increased, and the related load balancing technology can improve the data information processing pressure of the online education server to some extent, but it is difficult to fundamentally improve the service interaction efficiency of the online education server and the online education intelligent devices.

Based on the above, the inventors found through research and analysis that, in order to improve the efficiency of the business interaction between the online education server and the online education intelligent device, optimization and upgrade of the online education business service of the online education server need to be considered and further improvement on the allocation of the teaching resource information needs to be performed.

Disclosure of Invention

One of the embodiments of the present application provides an online education information processing method using big data, the method being applied to an online education server, the online education server being in communication connection with at least one online education terminal, the method including:

obtaining a current group of teaching resource data aiming at online education course information; each same teaching subject target teacher resource state information of each group of teaching resource data corresponds to a group of resource allocation strategies, and each group of resource allocation strategies comprises a set number of first resource allocation strategies preset according to the updating condition of the teaching resource data of the online education course information; the updating condition of the teaching resource data of the online education course information is the real-time updating condition of the classroom interaction data with student information change in the online education course information;

matching teaching state characteristics of the current teaching resource state information with a group of resource allocation strategies corresponding to the current teaching resource state information aiming at each current teaching resource state information in the current group of teaching resource data; and determining the resource information to be allocated for the at least one online education terminal according to the obtained matching result.

In some preferred embodiments, the determining resource information to be allocated for the at least one online education terminal according to the obtained matching result includes:

if teaching state characteristic of current teacher's resource state information with arbitrary first resource allocation strategy matches successfully in the set of resource allocation strategy that current teacher's resource state information corresponds, then according to teaching state characteristic update of current teacher's resource state information match the teaching state characteristic of the first resource allocation strategy that succeeds, and will current teacher's resource state information is confirmed to be first teacher's resource state information that awaits distribution.

In some preferred embodiments, the method further comprises:

if the teaching state characteristics of the current teaching state information are unsuccessfully matched with all first resource allocation strategies in a group of resource allocation strategies corresponding to the current teaching state information, selecting one first resource allocation strategy from the group of resource allocation strategies corresponding to the current teaching state information, modifying the teaching state characteristics of the selected first resource allocation strategy, and determining first candidate teaching state information according to the current teaching state information;

determining whether the current group of teaching resource data contains first candidate teacher resource state information;

and determining whether the candidate teaching resources to be distributed exist in the current group of teaching resource data or not according to whether the current group of teaching resource data contains first candidate teaching resource state information or not.

In some preferred embodiments, before the step of matching teaching state characteristics of the current teaching resource state information with a set of resource allocation policies corresponding to the current teaching resource state information for each current teaching resource state information in the current set of teaching resource data, the method further comprises:

carrying out data sampling on the obtained current group of teaching resource data according to a preset first data sampling model;

screening the current group of teaching resource data after data sampling to obtain first teaching resource data;

aim at every current teachers ' resource state information in the resource data of giving lessons of present group will the teaching state characteristic of current teachers ' resource state information with a set of resource allocation strategy that current teachers ' resource state information corresponds carries out the step of matching, includes:

aiming at each current teacher resource state information in the first teaching resource data, the teaching state characteristics of the current teacher resource state information are matched with a group of resource allocation strategies corresponding to the current teacher resource state information.

In some preferred embodiments, the teaching state characteristics of the current teacher state information include a teacher state score of the current teacher state information;

before the step of matching teaching state characteristics of the current teacher state information with a set of resource allocation policies corresponding to the current teacher state information, the method further comprises:

acquiring a teacher resource state score of each first teacher resource state information in a preset teaching time period corresponding to the current teacher resource state information aiming at each current teacher resource state information in the current group of teaching resource data;

obtaining a preset course evaluation statistical result of the current teacher state score and a preset course evaluation statistical result of the average teacher state score;

determining an average teacher status score corresponding to the preset teaching time period according to the teacher status score of the current teacher status information and the teacher status score of each first teacher status information;

determining first course evaluation statistical content corresponding to the current teacher resource state information from the course evaluation statistical results of the current teacher resource state scores;

obtaining second course evaluation statistical content corresponding to the determined average teacher state score from the course evaluation statistical result of the average teacher state score;

determining a resource allocation quantitative value of a comparison result of the first course evaluation statistical content and the second course evaluation statistical content;

adjusting the teacher state score of the current teacher state information according to the resource allocation quantization value of the determined comparison result;

will the teaching state characteristic of current teacher's resource state information with the step that a set of resource allocation strategy that current teacher's resource state information corresponds carries out the matching includes:

and matching the adjusted master state score of the current master state information with a group of resource allocation strategies corresponding to the adjusted current master state information.

In some preferred embodiments, the step of determining whether there is a candidate teaching resource to be allocated in the current set of teaching resource data according to whether the current set of teaching resource data includes first candidate teaching resource state information includes:

if the current group of teaching resource data contains first candidate teaching resource state information, determining that candidate teaching resources to be distributed exist in the current group of teaching resource data;

if the current group of teaching resource data does not contain first candidate teaching resource state information, determining that no candidate teaching resource to be distributed exists in the current group of teaching resource data;

after the step of determining that the candidate teaching resources to be allocated exist in the current set of teaching resource data, the method further includes:

determining candidate teaching resource data corresponding to the current group of teaching resource data and containing at least one first candidate teaching resource state information, and taking the candidate teaching resource data as first candidate teaching resource data;

and determining at least one candidate teaching resource to be allocated in the current group of teaching resource data according to the first candidate teaching resource data.

In some preferred embodiments, each first resource allocation strategy in each group of resource allocation strategies is a resource allocation strategy based on online classroom interaction, the teaching state characteristics of each first resource allocation strategy include first teacher teaching state characteristics, first student feedback state characteristics and bidirectional teaching score values, and the teaching state characteristics of the current teacher resource state information include the teacher resource state score of the current teacher resource state information;

aiming at each current teacher resource state information in the current teaching resource data set, respectively correcting the bidirectional teaching credit value of each first resource allocation strategy contained in a set of resource allocation strategies corresponding to the current teacher resource state information to obtain the corrected bidirectional teaching credit value of each first resource allocation strategy;

sequencing the first resource allocation strategies contained in a group of resource allocation strategies corresponding to the current teacher resource state information according to the corrected two-way teaching credit values of each first resource allocation strategy, wherein the sequencing numbers of each first resource allocation strategy are positively correlated with the corresponding corrected two-way teaching credit values;

the step of updating the teaching state characteristics of the successfully matched first resource allocation strategy according to the teaching state characteristics of the current teacher resource state information comprises the following steps:

updating the first teacher teaching state characteristic and the first student feedback state characteristic of the successfully matched first resource allocation strategy by using the teacher state score of the current teacher state information;

determining the bidirectional resource allocation heat according to the corrected bidirectional teaching score value of the successfully matched first resource allocation strategy;

according to the sequencing result of the set number of first resource allocation strategies, matching the teacher state score of the current teacher state information with each first resource allocation strategy in sequence, wherein when the teacher state score of the current teacher state information and the current first resource allocation strategy for matching meet a first set condition, the teacher state score of the current teacher state information is represented to be matched with the current first resource allocation strategy for matching, and otherwise, the teacher state score of the current teacher state information is not matched with the current first resource allocation strategy for matching;

the first setting condition is as follows: present teacher's resource state score of teacher's resource state information with the resource allocation quantization value of the target matching result of the first teacher state feature of giving lessons of the first resource allocation strategy of matching carries out at present is less than and sets for the proportion the current resource allocation reference value of the first student feedback state feature of the first resource allocation strategy of matching carries out, just the current two-way teaching score value of carrying out the first resource allocation strategy of matching is greater than the first threshold value of setting for, the first threshold value of setting for is: and determining a judgment value according to the bidirectional resource allocation heat and the updating condition of the teaching resource data of the online education course information.

In some preferred embodiments, the instructional state characteristics of each first resource allocation strategy further comprises instructional content hop times;

respectively adding one to the teaching content jump times of a first resource allocation strategy contained in a group of resource allocation strategies corresponding to the current teaching resource state information aiming at each current teaching resource state information in the current group of teaching resource data;

before the step of determining the current teaching resource state information as the first teaching resource state information to be distributed, the method further includes:

judging whether the number of jumping of the teaching content of the successfully matched first resource allocation strategy is less than a set number of jumping after adding one;

and if not, executing the step of determining the current teacher state information as the first teacher state information to be distributed.

In some preferred embodiments, the step of selecting a first resource allocation policy from a group of resource allocation policies corresponding to the current teacher state information and modifying the teaching state characteristics of the selected first resource allocation policy includes:

selecting a first resource allocation strategy with the minimum bidirectional teaching credit value from a group of resource allocation strategies corresponding to the current teacher state information;

modifying the first teacher teaching state characteristic of the selected first resource allocation strategy by using the teacher state score of the current teacher state information;

modifying the feedback state characteristic of the first student of the selected first resource allocation strategy into a classroom interaction state characteristic;

determining the bidirectional resource allocation heat according to the corrected bidirectional teaching score value of the selected first resource allocation strategy;

and setting the jumping times of the teaching contents of the selected first resource allocation strategy as target jumping times.

One of the embodiments of the present application provides a computer-readable storage medium on which a computer program is stored, which when executed implements the method described above.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals refer to like structures, wherein:

FIG. 1 is a flow diagram illustrating an exemplary method and/or process for online educational information processing using big data, in accordance with some embodiments of the present invention;

fig. 2 is a block diagram illustrating an exemplary online education information processing apparatus applying big data according to some embodiments of the present invention;

FIG. 3 is a block diagram of an exemplary online education information processing system applying big data, according to some embodiments of the present invention, an

Fig. 4 is a schematic diagram of the hardware and software components in an exemplary online education server according to some embodiments of the invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

To solve the technical problems described in the background art, the present invention provides an online education information processing method using big data and a computer-readable storage medium, which can analyze different teaching resource data of online education course information, thereby taking the teacher state information, the teaching state characteristics and the classroom interaction data into consideration, determining the resource information to be distributed aiming at different online education terminals by combining the corresponding resource distribution strategies, thus, the teaching resource information can be sent for different online education terminals in a targeted manner as far as possible on the premise of ensuring the stable operation of the different online education terminals, therefore, the interactive effectiveness of the online education terminal and the online education server is improved, and the online education server can optimize and upgrade the related online education service according to the related data fed back by the online education terminal.

Based on the above, first, an exemplary method for processing online education information using big data is described, referring to fig. 1, which is a flowchart illustrating an exemplary method and/or process for processing online education information using big data according to some embodiments of the present invention, the method for processing online education information using big data may be applied to an online education server communicatively connected to at least one online education terminal, and further, the method may include the technical solutions described in the following steps S110 and S120.

And S110, obtaining the current group of teaching resource data aiming at the online education course information.

In a related embodiment, the online education course information may be course information and resource allocation information of a related video network course configured by the online education server according to the course reservation information uploaded by the online education terminal, for example, the resource state information of each identical teaching subject of each set of teaching resource data may correspond to a set of resource allocation policies, and each set of resource allocation policies includes a set number of first resource allocation policies preset according to the update conditions of the teaching resource data of the online education course information. Furthermore, the update condition of the teaching resource data of the online education course information is a real-time update condition of classroom interaction data with student information change in the online education course information. The teaching subjects may include many stages of subjects, such as an elementary school subject, a junior school subject, a high school subject, a college subject, or a professional training subject, which is not limited herein. The teacher resource state information can comprise course arrangement information, teaching evaluation information, teacher qualification information and the like of different teachers, the resource allocation strategy can be used for instructing the online education server to allocate relevant teaching resources, such as teacher resource allocation or teaching content allocation and the like, the updating situation of teaching resource data is mainly focused on the real-time updating situation of classroom interaction data with student information change, for example, the change of the college information may be a change of the online status of the students or a change of the number of the students, etc., the classroom interaction data may be generally obtained by the online education server according to the interaction log of the online education terminal and/or the teacher teaching terminal, for some recorded and broadcast courses, the online education terminal can be analyzed only, and for some live broadcast courses, the online education terminal and the teacher teaching terminal can be analyzed simultaneously. Therefore, the teaching resource data of the online education course information can obtain richer teaching information, so that a decision basis is provided for subsequent resource allocation. For example, different sets of teaching resource data of the online education course information may be recorded according to a time sequence, for example, the teaching resource data corresponding to the online education course information may be { d1, d2, d3, d4}, that is, the online education course information may correspond to the first set of teaching resource data d1, the second set of teaching resource data d2, the third set of teaching resource data d3, and the fourth set of teaching resource data d4 in some time periods. Of course, in the actual real-time process, other more teaching resource data may also be included, which is not limited herein.

S120, aiming at each current teacher resource state information in the current teaching resource data set, matching teaching state characteristics of the current teacher resource state information with a set of resource allocation strategies corresponding to the current teacher resource state information; and determining the resource information to be allocated for the at least one online education terminal according to the obtained matching result.

In the related embodiment, by matching the teaching state characteristics of the current teacher state information with a set of resource allocation strategies corresponding to the current teacher state information, comprehensive analysis can be performed based on the course content level, teacher-student interaction level and the equipment operation level of the online education terminal, so as to obtain a corresponding matching result, since the matching result considers the interaction log of the online education terminal and/or teacher teaching terminal (i.e. the interaction stability of the online education terminal) and also considers the teaching state characteristics of the current teacher state information (i.e. teacher-student interaction situation), thus the adaptability of the obtained resource information to be allocated to the online education terminal can be ensured, and the resource information can be specifically and underground issued to different online education terminals as much as possible on the premise of ensuring the stable operation of different online education terminals, therefore, the interactive effectiveness of the online education terminal and the online education server is improved, and the online education server can optimize and upgrade the related online education service according to the related data fed back by the online education terminal.

On the basis of the above, different matching results may correspond to different resource information to be allocated, and in order to ensure the adaptability of the obtained resource information to be allocated to the online education terminal in the data communication layer and the course education layer, the different matching results need to be analyzed and processed.

The first condition, if the teaching state characteristic of current teacher's resource state information with arbitrary first resource allocation strategy matches successfully in the set of resource allocation strategy that current teacher's resource state information corresponds, then according to the teaching state characteristic of current teacher's resource state information is updated match the teaching state characteristic of the first resource allocation strategy that succeeds, and will current teacher's resource state information determines for first teacher's resource state information that awaits the distribution. In the actual implementation process, the teaching state characteristic of current teacher's resource state information with in the a set of resource allocation strategy that current teacher's resource state information corresponds arbitrary first resource allocation strategy match successfully can be confirmed with the matching coefficient that corresponds resource allocation strategy according to teaching state characteristic, for example, the matching coefficient can be considered the teacher's resource condition comprehensively, the course condition and the operational aspect at online education terminal, if the matching coefficient is greater than the setting coefficient, then can judge teaching state characteristic of current teacher's resource state information with arbitrary first resource allocation strategy matches successfully in the a set of resource allocation strategy that current teacher's resource state information corresponds, furtherly, confirms current teacher's resource state information as first teacher's resource state information that awaits distribution, can follow-up according to the course information that first teacher's resource state information that awaits distribution corresponds, can ensure like this that follow-up fully considers online education terminal's course information when allocating education resource to online education terminal The device communication stability and the teachers and materials course matching. In addition, the teaching state characteristics of the first resource allocation strategy which is successfully matched are updated, so that the timeliness of subsequent matching can be ensured.

In the second situation, if the teaching state characteristics of the current teaching state information are unsuccessfully matched with all first resource allocation strategies in a group of resource allocation strategies corresponding to the current teaching state information, selecting a first resource allocation strategy from the group of resource allocation strategies corresponding to the current teaching state information, modifying the teaching state characteristics of the selected first resource allocation strategy, and determining first candidate teaching state information according to the current teaching state information; determining whether the current group of teaching resource data contains first candidate teacher resource state information; and determining whether the candidate teaching resources to be distributed exist in the current group of teaching resource data or not according to whether the current group of teaching resource data contains first candidate teaching resource state information or not. Correspondingly, through the teaching state characteristic of revising first resource allocation strategy, can adjust the relevant distribution instruction that conflicts with the current situation in the resource allocation strategy, according to first candidate teacher's state information is confirmed to current teacher's state information, can ensure that first candidate teacher's state information and the online education terminal's under the current teaching state network communication state phase-match, ensures first candidate teacher's state information and real-time teaching demand phase-match simultaneously, according to whether current group's resource data of giving lessons contains first candidate teacher's state information, confirms whether there is the candidate in the resource data of giving lessons of current group to treat the distribution teaching resource, can ensure that the candidate that obtains treats the distribution teaching resource and matches with online service terminal as far as possible, avoids issuing wrong teaching resource and the wasting of resources that causes many times.

In a related embodiment, before the step "determining whether there is a candidate teaching resource to be allocated in the current set of teaching resource data according to whether the current set of teaching resource data includes first candidate teaching resource state information", the following may be included: if the current group of teaching resource data contains first candidate teaching resource state information, determining that candidate teaching resources to be distributed exist in the current group of teaching resource data; and if the current group of teaching resource data does not contain first candidate teaching resource state information, determining that no candidate teaching resource to be distributed exists in the current group of teaching resource data. Further, after the step "determining that there are candidate teaching resources to be allocated in the current set of teaching resource data", the method may further include the following steps: determining candidate teaching resource data corresponding to the current group of teaching resource data and containing at least one first candidate teaching resource state information, and taking the candidate teaching resource data as first candidate teaching resource data; and determining at least one candidate teaching resource to be allocated in the current group of teaching resource data according to the first candidate teaching resource data. For example, the candidate teaching resource data (course content data) of at least one first candidate teaching resource status information satisfying the requirement of the lesson period can be determined as the first candidate teaching resource data, and then at least one candidate teaching resource (such as teacher resource and/or course resource) to be allocated in association with the first candidate teaching resource data can be determined from the current set of teaching resource data. In an optional embodiment, before the step "determining at least one candidate teaching resource to be allocated in the current set of teaching resource data according to the first candidate teaching resource data", the following may be further included: matching teaching state characteristics of the current teaching resource state information with a preset second resource allocation strategy corresponding to the current teaching resource state information aiming at each current teaching resource state information in the current group of teaching resource data, wherein each teaching resource state information of the same teaching subject of each group of teaching resource data corresponds to one second resource allocation strategy; if the teaching state characteristics of the current teaching state information are successfully matched with the second resource allocation strategy, determining the current teaching state information as second teaching state information to be allocated, and updating the teaching state characteristics of the second resource allocation strategy according to the teaching state characteristics of the current teaching state information; if the teaching state characteristics of the current teaching information and the second resource allocation strategy fail to be matched, determining the current teaching information as second candidate teaching information; determining whether the current group of teaching resource data contains second candidate teacher resource state information; and when determining that the current group of teaching resource data contains second candidate teaching resource state information, determining candidate teaching resource data which correspond to the current group of teaching resource data and contain at least one second candidate teaching resource state information, and taking the candidate teaching resource data as second candidate teaching resource data. Correspondingly, the step "determining at least one candidate teaching resource to be allocated in the current set of teaching resource data according to the first candidate teaching resource data" may include the following: and determining candidate teaching resources to be distributed in the current set of teaching resource data according to at least one first candidate teaching resource state information contained in the first candidate teaching resource data and at least one second candidate teaching resource state information contained in the second candidate teaching resource data. For example, at least one first candidate teacher state information that first candidate teaching resource data contained with at least one second candidate teacher state information that second candidate teaching resource data contained exists the difference, for example at least one first candidate teacher state information that first candidate teaching resource data contained is to teacher's qualifications, at least one second candidate teacher state information that second candidate teaching resource data contained is to teacher's period of giving lessons, so design, can consider different candidate teacher state information from a plurality of dimensions, thereby ensure that the candidate in the present group of teaching resource data is waited to distribute teaching resource and is satisfied online education terminal's business demand as far as in time. In some selective embodiments, preset second resource allocation policy is a resource allocation policy based on online classroom interaction, the teaching state feature of second resource allocation policy includes second teacher teaching state feature and second student feedback state feature, the teaching state feature of current teacher state information includes the teaching state score of current teacher state information, based on this, the step "according the teaching state feature of current teacher state information updates the teaching state feature of second resource allocation policy" can include: and updating the second teacher teaching state characteristic and the second student feedback state characteristic of the second resource allocation strategy according to the teacher state score of the current teacher state information. For example, the teaching state characteristics of the second teacher and the feedback state characteristics of the second student of the second resource allocation strategy can be updated in a differentiated manner according to the level of the teacher state score of the current teacher state information, so that the high correlation between the updating of the teaching state characteristics of the second teacher and the feedback state characteristics of the second student and the teacher state score can be ensured.

In some optional embodiments, after the step "determining that there are candidate teaching resources to be allocated in the current set of teaching resource data", the method further comprises: obtaining the previous N groups of teaching resource data associated with the current group of teaching resource data, wherein N is a set positive integer; determining resource response performance of the candidate teaching resources to be distributed according to the obtained front N groups of teaching resource data and the current group of teaching resource data; determining whether the matching degree of the candidate teaching resources to be distributed in the teaching resources in the online education course information is lower than a set matching degree according to the determined resource response performance; and when the matching degree of the candidate teaching resources to be distributed in the teaching resources in the online education course information is lower than the set matching degree, outputting resource distribution prompt information. The resource response performance of the teaching resources to be distributed is used for representing the equipment performance of the corresponding online education terminal, for example, whether the online education terminal can play a related online education video or not, correspondingly, the matching degree of the teaching resources in the online education course information of the candidate teaching resources to be distributed can be understood as the response capacity of the corresponding online education terminal to the candidate teaching resources to be distributed, so that when the matching degree of the teaching resources in the online education course information of the candidate teaching resources to be distributed is lower than the set matching degree, the online education terminal is difficult to effectively respond and process the candidate teaching resources to be distributed, and based on the result, the online education server can output resource distribution prompt information to the online education terminal to prompt the online education terminal to perform related software and hardware upgrading.

In some optional embodiments, before the step "determining the teaching resource to be allocated as a candidate in the current set of teaching resource data according to at least one first candidate teaching resource state information included in the first candidate teaching resource data and at least one second candidate teaching resource state information included in the second candidate teaching resource data", the method may further include the following steps: performing data sampling on the second candidate teaching resource data according to a preset third data sampling model to obtain third teaching resource data; and carrying out teaching resource screening on the first candidate teaching resource data according to a preset first teaching resource screening condition to obtain fourth teaching resource data. The preset third data sampling model may also be a machine learning model, the preset first teaching resource screening condition may be formulated based on the regional distribution result of the teaching resources, for example, the distribution of the teaching resources is specific to a certain region, so that the regional distribution result of the teaching resources tends to be concentrated, and the first teaching resource screening condition may be designed according to the geographical location distribution of the online education terminal. Therefore, the first candidate teaching resource data and the second candidate teaching resource data can be further processed, the data processing efficiency of the online education server is improved, and the data processing pressure of the online education server is reduced. Based on this, the step of "determining the candidate teaching resources to be allocated in the current set of teaching resource data according to at least one first candidate teaching resource state information included in the first candidate teaching resource data and at least one second candidate teaching resource state information included in the second candidate teaching resource data" may include: and determining candidate teaching resources to be allocated in the current group of teaching resource data according to the third teaching resource data and the fourth teaching resource data. In some alternative embodiments, the step of "determining candidate teaching resources to be allocated in the current set of teaching resource data according to the third teaching resource data and the fourth teaching resource data" may include the following steps: determining third candidate teaching resource data according to the third teaching resource data and the fourth teaching resource data; performing teaching resource screening on the third candidate teaching resource data by using a preset second teaching resource screening condition to obtain fifth teaching resource data; and identifying the fifth teaching resource data by using a preset resource association identification model to obtain at least one candidate teaching resource to be distributed in the current group of teaching resource data. It can be understood that the resource association identification model can search and summarize related resource data of the fifth teaching resource data, so as to determine candidate teaching resources to be allocated as completely as possible.

In an actual implementation process, the step "selecting a first resource allocation policy from a group of resource allocation policies corresponding to the current teacher resource state information, and modifying teaching state characteristics of the selected first resource allocation policy" may include: selecting a first resource allocation strategy with the minimum bidirectional teaching credit value from a group of resource allocation strategies corresponding to the current teacher state information; modifying the first teacher teaching state characteristic of the selected first resource allocation strategy by using the teacher state score of the current teacher state information; modifying the feedback state characteristic of the first student of the selected first resource allocation strategy into a classroom interaction state characteristic; determining the bidirectional resource allocation heat according to the corrected bidirectional teaching score value of the selected first resource allocation strategy; and setting the jumping times of the teaching contents of the selected first resource allocation strategy as target jumping times. So design, can carry out the modification of the teaching state characteristic of first resource allocation strategy based on two-way teaching score value, teacher's resource state score to ensure to modify the in-process mutual adaptation of teacher end and student end, and then ensure the global stability of the teaching state characteristic of first resource allocation strategy.

In the practical implementation process, the inventor finds that, along with the continuous expansion of the scale of online education, the data processing pressure faced by the online education server is increasingly greater, and in order to reduce the data processing pressure of the online education server, before the step of "aiming at each current teacher status information in the current set of teaching resource data, the teaching status characteristics of the current teacher status information are matched with a set of resource allocation strategies corresponding to the current teacher status information", the method may further include the following contents: carrying out data sampling on the obtained current group of teaching resource data according to a preset first data sampling model; and screening the current group of teaching resource data after data sampling to obtain first teaching resource data. In a related embodiment, the preset first data sampling model may be a Machine Learning (Machine Learning) -based Neural Network (NN) or a Support Vector Machine (SVM) model, and is not limited herein. The data sampling is carried out on the current group of teaching resource data, some non-teaching service type redundant data can be filtered out, so that the data volume is reduced, the current group of teaching resource data after the data sampling is subjected to screening processing, some abnormal teaching resource data (such as noise data generated due to network communication disturbance) can be filtered out, so that the data volume is further reduced, the current group of teaching resource data can be simplified on the premise of ensuring the data accuracy and the availability of the current group of teaching resource data, and the data processing pressure of an online education server is reduced. On this basis, above-mentioned step "aim at every current teacher's resource state information in the resource data of giving lessons of current group will teaching state characteristic of current teacher's resource state information with a set of resource allocation strategy that current teacher's resource state information corresponds matches", can realize through following mode: aiming at each current teacher resource state information in the first teaching resource data, the teaching state characteristics of the current teacher resource state information are matched with a group of resource allocation strategies corresponding to the current teacher resource state information. So design, because the teaching state characteristic of present teacher's resource state information is confirmed through first resource data of giving lessons, therefore the teaching state characteristic of present teacher's resource state information has lower noise ratio, can ensure like this the teaching state characteristic of present teacher's resource state information with the matching accuracy and the credibility of a set of resource allocation strategy that present teacher's resource state information corresponds can also reduce online education server's operating pressure.

In some optional embodiments, before the step "matching teaching state characteristics of the current teaching resource state information with a set of resource allocation policies corresponding to the current teaching resource state information for each current teaching resource state information" the method may further include: and carrying out data sampling on the current group of teaching resource data according to a preset second data sampling model to obtain second teaching resource data. The preset second data sampling model may integrate functions of data sampling, data classification, and data screening, so that efficiency of preprocessing a current set of teaching resource data may be improved, for example, the second data sampling model may be deployed in an associated server, and the data sampling function may be implemented by calling the second data sampling model when in use. Based on this, the step "match the teaching state characteristics of the current teaching state information with a set of resource allocation policies corresponding to the current teaching state information for each current teaching state information in the current set of teaching resource data", may include the following: aiming at each current teacher resource state information in the second teaching resource data, the teaching state characteristics of the current teacher resource state information are matched with a group of resource allocation strategies corresponding to the current teacher resource state information. So design, because the teaching state characteristic of current teacher's resource state information is confirmed through the second resource data of giving lessons, therefore the teaching state characteristic of current teacher's resource state information has lower noise ratio, can ensure like this the teaching state characteristic of current teacher's resource state information with the matching accuracy and the credibility of a set of resource allocation strategy that current teacher's resource state information corresponds can also reduce online education server's operating pressure.

In a related embodiment, every first resource allocation strategy in every resource allocation strategy of group is the resource allocation strategy based on online classroom interaction, and the teaching state characteristic of every first resource allocation strategy all includes first teacher state characteristic of giving lessons, first student feedback state characteristic and two-way teaching value of assessing, the teaching state characteristic of current teacher state information includes the teacher state of assessing of current teacher state information. The teaching state characteristics of the first teacher, the feedback state characteristics of the first students and the bidirectional teaching score values can be used for evaluating the teaching states, the teaching state characteristics of the first teacher and the feedback state characteristics of the first students can be evaluated through multiple dimensions, the bidirectional teaching score values can be the score values obtained through calculation of a set algorithm after mutual evaluation of the teacher and the students, and the two-way teaching score values can be generally designed to be 0-100. On this basis, before the step of "matching teaching state characteristics of the current teaching state information with a set of resource allocation policies corresponding to the current teaching state information", the method may further include the following steps: aiming at each current teacher resource state information in the current teaching resource data set, respectively correcting the bidirectional teaching credit value of each first resource allocation strategy contained in a set of resource allocation strategies corresponding to the current teacher resource state information to obtain the corrected bidirectional teaching credit value of each first resource allocation strategy; and sequencing the first resource allocation strategies contained in a group of resource allocation strategies corresponding to the current teacher resource state information according to the corrected two-way teaching credit values of each first resource allocation strategy, wherein the sequencing numbers of each first resource allocation strategy are positively correlated with the corresponding corrected two-way teaching credit values. By correcting the bidirectional teaching score value of each first resource allocation strategy, scores corresponding to subjective moods can be removed as far as possible, so that the obtained bidirectional teaching score value can reflect the teaching state as objectively as possible. On the basis of the above, the step "updating the teaching state characteristics of the successfully matched first resource allocation strategy according to the teaching state characteristics of the current teacher resource state information" may include the following steps: updating the first teacher teaching state characteristic and the first student feedback state characteristic of the successfully matched first resource allocation strategy by using the teacher state score of the current teacher state information; and determining the bidirectional resource allocation heat according to the corrected bidirectional teaching score value of the successfully matched first resource allocation strategy. For example, the bidirectional resource allocation heat degree can be understood as a weighted value of the heat degree of the related teaching resource allocated to the teacher device side and the heat degree of the related teaching resource allocated to the student device side. Further, in some optional embodiments, the step of "matching teaching state characteristics of the current teaching state information with a set of resource allocation policies corresponding to the current teaching state information" may include the following: according to the sequencing result of setting for a quantity of first resource allocation strategy will the master-slave state score of current master-slave state information matches with every first resource allocation strategy in proper order, wherein, works as when the master-slave state score of current master-slave state information satisfies first settlement condition with the first resource allocation strategy that matches at present, then the sign the master-slave state score of current master-slave state information with the matching of the first resource allocation strategy that matches is carried out at present, otherwise, then mismatching. For example, the first setting condition may be: present teacher's resource state score of teacher's resource state information with the resource allocation quantization value of the target matching result of the first teacher state feature of giving lessons of the first resource allocation strategy of matching carries out at present is less than and sets for the proportion the current resource allocation reference value of the first student feedback state feature of the first resource allocation strategy of matching carries out, just the current two-way teaching score value of carrying out the first resource allocation strategy of matching is greater than the first threshold value of setting for, the first threshold value of setting for is: and determining a judgment value according to the bidirectional resource allocation heat and the updating condition of the teaching resource data of the online education course information. For example, the resource allocation quantization value is used to represent the feasibility degree of teaching resource allocation, for example, the larger the resource allocation quantization value is, the higher the feasibility of teaching resource allocation is. By the design, the matching reliability of the teaching state characteristics of the current teacher state information and a group of resource allocation strategies corresponding to the current teacher state information can be ensured. In a related embodiment, the "target matching result of the teacher status score of the current teacher status information and the first teacher lecture status feature of the current matching first resource allocation policy" and the matching result of the above S120 may be understood as different matching results.

In some possible embodiments, the teaching state characteristic of each first resource allocation policy further includes a teaching content skip time, and based on this, before the step of "matching the teaching state characteristic of the current teaching resource state information with a set of resource allocation policies corresponding to the current teaching resource state information", the following may be included: and aiming at each current teacher resource state information in the current teaching resource data group, respectively adding one to the teaching content jump times of a first resource allocation strategy contained in a group of resource allocation strategies corresponding to the current teacher resource state information. Wherein, the number of times is jumped to the teaching content can be understood as the number of times that online education video was switched to online education terminal, and the number of times is jumped to the teaching content through with first resource allocation strategy adds one, can consider the maloperation of online education terminal in the interactive process, certainly, the number of times is jumped to the teaching content can also add two or add three according to actual conditions, based on this, "will current teaching material state information determines for first teaching material state information that needs the distribution", include: judging whether the number of jumping of the teaching content of the successfully matched first resource allocation strategy is less than a set number of jumping after adding one; and if not, executing the step of determining the current teacher state information as the first teacher state information to be distributed. The set jump times can be adjusted according to the teaching feedback condition of the actual trainee, and is not limited herein. By the design, the first to-be-distributed teacher and resource state information can be ensured to be matched with the actual teaching requirements of the students as far as possible according to the jumping times of the teaching contents.

In some possible embodiments, the teaching state characteristics of the current teaching state information include the teaching state score of the current teaching state information, and based on this, before "will" the teaching state characteristics of the current teaching state information match with a set of resource allocation policies corresponding to the current teaching state information ", the following may be included: aiming at each current teacher resource state information in the current group of teaching resource data, obtaining a teacher resource state score of each first teacher resource state information in a preset teaching time period corresponding to the current teacher resource state information; obtaining a preset course evaluation statistical result of the current teacher state score and a preset course evaluation statistical result of the average teacher state score; determining an average teacher status score corresponding to the preset teaching time period according to the teacher status score of the current teacher status information and the teacher status score of each first teacher status information; determining first course evaluation statistical content corresponding to the current teacher resource state information from the course evaluation statistical results of the current teacher resource state scores; obtaining second course evaluation statistical content corresponding to the determined average teacher state score from the course evaluation statistical result of the average teacher state score; determining a resource allocation quantitative value of a comparison result of the first course evaluation statistical content and the second course evaluation statistical content; and adjusting the teacher state score of the current teacher state information according to the resource allocation quantization value of the determined comparison result. So design, including considering the course aassessment statistical result that predetermines the period of time of giving lessons and average master and fund state score that current master and fund state information corresponds, can accurately confirm first course aassessment statistical content with the resource allocation quantitative value of the comparison result of second course aassessment statistical content can be right like this the master and fund state score of current master and fund state information adjusts the credibility that the master and fund state score of ensureing current master and fund state information scored. Based on this, the step of "matching the teaching state characteristics of the current teaching state information with a set of resource allocation policies corresponding to the current teaching state information" may include the following: and matching the adjusted master state score of the current master state information with a group of resource allocation strategies corresponding to the adjusted current master state information. It can be understood that, for a further implementation of "matching the teacher status score of the adjusted current teacher status information with a set of resource allocation policies corresponding to the adjusted current teacher status information", reference may be made to the above-mentioned similar implementation, which is not described herein again.

In summary, in the implementation of the above solution, first, a current set of teaching resource data for online education course information is obtained, then, for each current teacher resource state information in the current set of teaching resource data, teaching state characteristics of the current teacher resource state information are matched with a set of resource allocation policies corresponding to the current teacher resource state information, and finally, resource information to be allocated for the online education terminal is determined according to an obtained matching result. By the design, different teaching resource data of online education course information can be analyzed, teacher state information, teaching state characteristics and classroom interaction data are taken into consideration, and corresponding resource allocation strategies are combined to determine resource information to be allocated for different online education terminals, so that the teaching resource information can be sent out to different online education terminals in a targeted manner as far as possible on the premise of ensuring stable operation of different online education terminals, the interactive effectiveness of the online education terminals and the online education server is improved, and the online education server optimizes and upgrades related online education service according to related data fed back by the online education terminals.

For example, after the teaching resource information is sent in a targeted manner, the online education terminal can perform objective and effective teaching interaction with the online education server, such as online answering and the like, so that the online education server can optimize and upgrade online education service according to classroom work data or teaching evaluation data fed back by the online education terminal, for example, the differentiated visualization processing of some course contents is performed so that the related course contents are easier to understand by students, and for example, the distribution area of some interactive function modules during display is adaptively adjusted, thereby avoiding the abnormality of the online education terminal caused by misoperation of the students during the use of the online education terminal. Therefore, by distributing the teaching resources, the optimization and the upgraded data source of the subsequent online education service can be guaranteed, and the intelligent degree of interaction between the online education server and the online education terminal is further improved.

In view of the above-described online education information processing method using big data, an exemplary online education information processing apparatus using big data is also provided in an embodiment of the present invention, and as shown in fig. 2, the online education information processing apparatus 200 using big data may include the following functional modules.

A data obtaining module 210, configured to obtain a current group of teaching resource data for online education course information; each same teaching subject target teacher resource state information of each group of teaching resource data corresponds to a group of resource allocation strategies, and each group of resource allocation strategies comprises a set number of first resource allocation strategies preset according to the updating condition of the teaching resource data of the online education course information; and the update condition of the teaching resource data of the online education course information is the real-time update condition of the classroom interaction data with student information change in the online education course information.

An information determining module 220, configured to match, for each current teacher state information in the current set of teaching resource data, teaching state characteristics of the current teacher state information with a set of resource allocation policies corresponding to the current teacher state information; and determining the resource information to be allocated aiming at the at least one online education terminal according to the obtained matching result.

Based on the above method embodiment and apparatus embodiment, the present invention further provides a system embodiment, that is, an online education information processing system using big data, and referring to fig. 3, an online education information processing system 300 using big data may include an online education server 310 and an online education terminal 320. In which the online education server 310 communicates with the online education terminal 320 to implement the above-described method, and further, the functionality of the online education information processing system 300 applying the big data is described as follows. The online education server 310 obtains a current set of teaching resource data for online education course information; each same teaching subject target teacher resource state information of each group of teaching resource data corresponds to a group of resource allocation strategies, and each group of resource allocation strategies comprises a set number of first resource allocation strategies preset according to the updating condition of the teaching resource data of the online education course information; the updating condition of the teaching resource data of the online education course information is the real-time updating condition of the classroom interaction data with student information change in the online education course information; matching teaching state characteristics of the current teaching resource state information with a group of resource allocation strategies corresponding to the current teaching resource state information aiming at each current teaching resource state information in the current group of teaching resource data; and determining resource information to be allocated for the at least one online education terminal 320 according to the obtained matching result.

Based on the above, referring to fig. 4, the online education server 310 may include a processing engine 311, a network module 312 and a memory 313, and the processing engine 311 and the memory 313 communicate through the network module 312.

Processing engine 311 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 311 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, Processing engine 311 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

The network module 312 may facilitate the exchange of information and/or data. In some embodiments, the network module 312 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 312 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 312 may include at least one network access point. For example, the network module 312 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 313 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 313 is configured to store a program, and the processing engine 311 executes the program after receiving the execution instruction.

It is to be understood that the structure shown in fig. 4 is only an illustration, and the online education server 310 may further include more or less components than those shown in fig. 4, or have a different configuration from that shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer readable storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer-readable storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application history document is inconsistent or conflicting with the present application as to the extent of the present claims, which are now or later appended to this application. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An online education information processing method using big data, applied to an online education server communicatively connected with at least one online education terminal, the method comprising:

and sequencing the first resource allocation strategies contained in a group of resource allocation strategies corresponding to the current teacher resource state information according to the corrected two-way teaching credit values of each first resource allocation strategy, wherein the sequencing numbers of each first resource allocation strategy are positively correlated with the corresponding corrected two-way teaching credit values.

2. The method of claim 1, further comprising:

obtaining a current group of teaching resource data aiming at online education course information; each same teaching subject target teacher resource state information of each group of teaching resource data corresponds to a group of resource allocation strategies, and each group of resource allocation strategies comprises a set number of first resource allocation strategies preset according to the updating condition of the teaching resource data of the online education course information; the updating condition of the teaching resource data of the online education course information is the real-time updating condition of the classroom interaction data with student information change in the online education course information; correspondingly: the online education course information is the course information and teacher and fund preparation information of relevant video network courses configured by the online education server according to the course reservation information uploaded by the online education terminal;

3. The method according to claim 2, wherein determining the resource information to be allocated for the at least one online education terminal according to the obtained matching result comprises:

if the teaching state characteristics of the current teaching state information are successfully matched with any first resource allocation strategy in a group of resource allocation strategies corresponding to the current teaching state information, updating the teaching state characteristics of the successfully matched first resource allocation strategies according to the teaching state characteristics of the current teaching state information, and determining the current teaching state information as first to-be-allocated teaching state information;

if the teaching state characteristics of the current teaching state information are unsuccessfully matched with all first resource allocation strategies in a group of resource allocation strategies corresponding to the current teaching state information, selecting one first resource allocation strategy from the group of resource allocation strategies corresponding to the current teaching state information, modifying the teaching state characteristics of the selected first resource allocation strategy, and determining first candidate teaching state information according to the current teaching state information; determining whether the current group of teaching resource data contains first candidate teacher resource state information; and determining whether the candidate teaching resources to be distributed exist in the current group of teaching resource data or not according to whether the current group of teaching resource data contains first candidate teaching resource state information or not.

4. The method according to claim 3, wherein each first resource allocation strategy in each set of resource allocation strategies is a resource allocation strategy based on online classroom interaction, the teaching status characteristics of each first resource allocation strategy comprises a first teacher teaching status characteristic, a first student feedback status characteristic and a two-way teaching score value, and the teaching status characteristics of the current teacher status information comprises the teacher status score of the current teacher status information.

5. The method according to claim 4, wherein the step of updating the teaching status characteristics of the successfully matched first resource allocation strategy according to the teaching status characteristics of the current teacher status information comprises:

and determining the bidirectional resource allocation heat according to the corrected bidirectional teaching score value of the successfully matched first resource allocation strategy.

6. The method as claimed in claim 5, wherein the step of matching teaching state characteristics of the current teaching state information with a set of resource allocation policies corresponding to the current teaching state information comprises:

7. The method of claim 6, wherein the instructional state characteristics of each first resource allocation strategy further comprises instructional content hop counts;

judging whether the number of jumping times of the teaching content of the successfully matched first resource allocation strategy is added by one and then is smaller than a set number of jumping times;

8. The method of claim 7, wherein the step of selecting a first resource allocation policy from a set of resource allocation policies corresponding to the current teacher state information and modifying the teaching state characteristics of the selected first resource allocation policy comprises:

9. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any one of claims 1-8.