CN113361393A - Big data processing method, server and storage medium for distance education - Google Patents
Big data processing method, server and storage medium for distance education Download PDFInfo
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Abstract
The embodiment of the application provides a big data processing method, a server and a storage medium for distance education, and the big data processing method, the server and the storage medium are used for acquiring target live course information; determining a first interaction condition of target live course information; performing course content optimization processing on first live course information included in the target live course information to obtain live course optimized content information; determining the interactive heat of the optimized content information of the live course; and determining the target interaction condition of the live course information of the identity information of the target course participants based on the interaction heat and the first interaction condition. By the method and the device, the problem that the recognition accuracy of the classroom interaction condition of the live course information is poor in the related technology is solved, and the effect of accurately determining the classroom interaction condition of the live course information is achieved.
Description
Technical Field
The present application relates to the field of distance education and big data technology, and in particular, to a big data processing method, server and storage medium for distance education.
Background
With the rapid development and popularization of electronic computers, particularly multimedia technology and network technology, new education contents and vitality are endowed, and the education forms are various. Briefly, the education of today is not just traditional ground-line face-to-face education, but more network education, that is, remote education through network communication, and the mode of such remote education will become more and more popular, and it is likely that remote education will become the main education means in the near future.
In order to ensure the teaching quality of distance education, classroom interaction analysis is generally required, but the related art has difficulty in accurately identifying classroom interaction conditions of live course information.
Disclosure of Invention
In order to solve the technical problems in the related art, the present application provides a big data processing method, a server, and a storage medium for distance education.
The application provides a big data processing method for distance education, which comprises the following steps:
acquiring target live course information;
determining a first interaction condition of the target live course information;
performing course content optimization processing on first live-broadcast course information included in the target live-broadcast course information to obtain live-broadcast course optimized content information, wherein the first live-broadcast course information includes target course participant identity information, and a course comparison result of the live-broadcast course information of the target course participant identity information and the first live-broadcast course information is greater than a first result description value;
determining the interactive heat of the live course optimized content information;
and determining the target interaction condition of the live course information of the identity information of the target course participants based on the interaction heat and the first interaction condition.
Optionally, determining a first interaction condition of the target live course information includes:
determining a second interaction condition of the target live course information based on a live course information identification strategy;
analyzing live course information of the target course participant identity information using a first information analysis network to determine a third interaction condition of the live course information of the target course participant identity information, the first information analysis network being trained through machine learning using a plurality of sets of first training sample data, wherein each set of training sample data in the plurality of sets of first training sample data includes: live course information and the interaction condition of the live course information;
and determining the second interaction condition and the third interaction condition as the first interaction condition.
Optionally, determining a target interaction condition of live course information of the identity information of the target course participant based on the interaction popularity and the first interaction condition includes:
analyzing the target category of the identity information of the target course participants included in the target live course information;
determining the attention of the live course optimization content information;
determining the target interaction situation based on the interaction heat, the target category, the attention, the second interaction situation and the third interaction situation.
Optionally, determining the attention of the live course optimized content information includes:
cleaning the live course optimized content information based on a set cleaning mode to determine a first course content change condition of the live course optimized content information in a first classroom interaction state and the biased live course information of the live course optimized content information;
determining the course content change weighting result of all course content change conditions of which the target course content change condition included in the partial live course information is larger than the second result description value;
acquiring a content statistical result of the live course optimized content information;
and determining a first course comparison result of the course content change weighting result and a target course content superposition result as the attention degree, wherein the target course content superposition result is a course content superposition result of the content statistical result and the first reference content.
Optionally, parsing a target category of the target course participant identity information included in the target live course information includes:
determining the category of the identity information of the target course participant based on a live course information identification strategy;
determining a first course knowledge point evaluation of live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is an individual object, and determining the target category as an active individual object on the premise that the first course knowledge point evaluation is greater than or equal to a third result description value;
determining the target category as an inactive individual subject on the premise that the first course knowledge point score is less than the third result description value;
determining a second course knowledge point evaluation of live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is a group object, and determining the target category as an active group object on the premise that the second course knowledge point evaluation is greater than or equal to a fourth result description value;
and on the premise that the evaluation of the second course knowledge point is smaller than the fourth result description value, determining that the target category is an inactive group object.
Optionally, determining the target interaction condition based on the interaction heat, the target category, the attention, the second interaction condition, and the third interaction condition includes:
weighting the attention degree, the second interaction condition and the third interaction condition to obtain a first weighting processing result, and determining a second course comparison result of the first weighting processing result and second reference content;
on the premise that the target class indicates that the class of the identity information of the target course participant is an active object, determining a course content superposition result of the interaction heat and the second course comparison result as the target interaction condition;
on the premise that the target class indicates that the class of the target course participant identity information is a small object, determining the evaluation score of the target course participant identity information, and determining the course content superposition result of the evaluation score, the interaction heat and the second course comparison result as the target interaction condition;
correspondingly, determining the evaluation score of the identity information of the target course participant comprises the following steps:
on the premise that the target course participant identity information is determined to be an individual object, determining a third course comparison result of the evaluation of the target course participant identity information and third reference content obtained through identification, and processing the third course comparison result through a first big data algorithm to obtain the evaluation score;
on the premise of determining that the target course participant identity information is a group object, acquiring a fourth course comparison result of the evaluation of a reference individual object and the evaluation of a reference group object, determining a fifth course comparison result of the evaluation of the target course participant identity information and fourth reference content, and processing a course content superposition result of the fourth course comparison result and the fifth course comparison result through a second big data algorithm to obtain the evaluation score.
Optionally, determining the interaction heat of the live course optimized content information includes:
obtaining a reference proportion index of the course participant identity information with the same category as the target course participant identity information;
determining a first proportion index of the identity information of the target course participant;
determining a first optimized value of a difference value between the reference ratio index and the first ratio index;
and determining a difference value between a fifth reference content and a sixth course comparison result as the interaction heat, wherein the sixth course comparison result is the course comparison result of the first optimized value and the reference proportion index.
Optionally, determining the interaction heat of the live course optimized content information includes:
determining two-classification partial live course information of the live course optimized content information in a second classroom interaction state;
acquiring the curriculum plate information of which the page click frequency is smaller than a fifth result description value and which is included in the two-classification partial live curriculum information based on a target adjustment strategy;
determining the curriculum plate information with the maximum page clicking frequency in the curriculum plate information as target curriculum plate information;
determining the page attention degree of the target course plate information based on a target big data algorithm;
determining a difference value between a sixth reference content and a seventh course comparison result as the interaction heat, wherein the seventh course comparison result is a course comparison result of the page attention degree of a target proportion value and a preset index;
correspondingly, after determining the target interaction condition of the live course information of the identity information of the target course participant based on the interaction heat and the first interaction condition, the method further comprises the following steps:
determining other interaction conditions of other live course information, wherein the other live course information and the target live course information are both obtained after analyzing the interaction state of the same course and comprise the live course information of the identity information of the target course participant;
determining the interaction condition with the maximum corresponding quantization value from the other interaction conditions and the target interaction condition, and determining the live course information corresponding to the interaction condition with the maximum corresponding quantization value as second live course information;
and determining the intention content and/or the requirement content of the course participant identity information included in the second live course information based on the second live course information.
The application also provides a data processing server, which comprises a processor and a memory; the processor is connected with the memory in communication, and the processor is used for reading the computer program from the memory and executing the computer program to realize the method.
The present application also provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the above-described method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
According to the method and the device, after the target live course information is obtained, the first interaction condition of the target live course information is determined, course content optimization processing is carried out on the first live course information included in the target live course information, live course optimization content information is obtained, the interaction heat of the live course optimization content information is determined, and the target interaction condition of the live course information of the identity information of the target course participants is determined according to the interaction heat and the first interaction condition. Because the course comparison result of the live course information of the identity information of the target course participant in the first live course information and the course of the first live course information is greater than the first result description value, the interactive heat of the live course information of the identity information of the target course participant can be accurately determined through the first live course information, the target interactive condition of the live course information of the identity information of the target course participant is comprehensively determined based on the interactive heat and the interactive condition of the target live course information, and the accuracy and the scene adaptability for determining the target interactive condition can be improved. Therefore, the problem that the accuracy of recognizing the classroom interaction condition of the live course information is poor in the related technology can be solved, and the effect of accurately determining the classroom interaction condition of the live course information is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic hardware structure diagram of a data processing server according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a big data processing method of distance education according to an embodiment of the present application.
Fig. 3 is a block diagram of a big data processing device for distance education according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a data processing server, a computer device, or a similar computing device. Taking an example of the operation on a data processing server, fig. 1 is a hardware block diagram of a data processing server implementing a big data processing method of distance education according to an embodiment of the present application. As shown in fig. 1, the data processing server 10 may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, the data processing server 10 may further include a transmission device 106 for communication function. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely an illustration, and does not limit the structure of the data processing server 10. For example, the data processing server 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a big data processing method of distance education in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the above-mentioned methods. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the data processing server 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the data processing server 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Referring to fig. 2, the present application provides a flow chart of a big data processing method for distance education, which may specifically include the technical solutions described in steps 210 to 250.
And step 210, acquiring target live course information.
In this embodiment, the target live-broadcast course information may be course information corresponding to online education and interaction based on the online education service.
Step 220, determining a first interaction condition of the target live course information.
In this embodiment, the first interaction scenario may be an interaction effect of online education based on an online education service, such as: the student and the student comment each other, and the teacher asks the student to answer.
In an exemplary example, the determining the first interaction condition of the target live-broadcast course information in step 220 may specifically include: determining a second interaction condition of the target live course information based on a live course information identification strategy; analyzing live course information of the target course participant identity information using a first information analysis network to determine a third interaction condition of the live course information of the target course participant identity information, the first information analysis network being trained through machine learning using a plurality of sets of first training sample data, wherein each set of training sample data in the plurality of sets of first training sample data includes: live course information and the interaction condition of the live course information; and determining the second interaction condition and the third interaction condition as the first interaction condition.
It is to be appreciated that the live lesson information recognition strategy can be a live lesson information recognition algorithm/model. The first, second and third interaction conditions are mainly used for distinguishing different interaction effects. The first information analysis network may be a machine learning model. Therefore, the target live course information is deeply identified according to the live course information identification algorithm and the machine model, and the first interaction condition of the target live course information can be accurately judged.
Step 230, performing course content optimization processing on the first live course information included in the target live course information to obtain live course optimized content information.
In this embodiment, the first live-action course information includes target course participant identity information, and a comparison result between the live-action course information of the target course participant identity information and the course of the first live-action course information is greater than a first result description value, and the target course participant identity information may be a user/student performing online learning. The live course optimization content information is used for representing a processing result obtained after the data processing server performs course content optimization processing on first live course information included in the target live course information.
And 240, determining the interactive heat of the optimized content information of the live course.
In this embodiment, the interaction heat may be an online classroom interaction preference for live course optimization content information teaching based on an online education service.
In an illustrative example, the determining the interactive heat of the live lesson optimization content information in step 240 may specifically include the following two embodiments.
Example A: obtaining a reference proportion index of the course participant identity information with the same category as the target course participant identity information; determining a first proportion index of the identity information of the target course participant; determining a first optimized value of a difference value between the reference ratio index and the first ratio index; and determining a difference value between a fifth reference content and a sixth course comparison result as the interaction heat, wherein the sixth course comparison result is the course comparison result of the first optimized value and the reference proportion index.
Example B: determining two-classification partial live course information of the live course optimized content information in a second classroom interaction state; acquiring the curriculum plate information of which the page click frequency is smaller than a fifth result description value and which is included in the two-classification partial live curriculum information based on a target adjustment strategy; determining the curriculum plate information with the maximum page clicking frequency in the curriculum plate information as target curriculum plate information; determining the page attention degree of the target course plate information based on a target big data algorithm; and determining a difference value between the sixth reference content and a seventh course comparison result as the interaction heat, wherein the seventh course comparison result is a course comparison result of the page attention degree of the target proportion value and a preset index.
Therefore, through any one of the two embodiments, the interaction heat of the live-broadcast course optimization content information can be accurately judged according to the difference value between the reference content and the course comparison result.
And 250, determining the target interaction condition of the live course information of the identity information of the target course participants based on the interaction heat and the first interaction condition.
In an exemplary example, the step 250 of determining the target interaction condition of the live course information of the identity information of the target course participant based on the interaction heat and the first interaction condition may specifically include the technical solutions described in the steps 2501 to 2503.
Step 2501, analyzing the target category of the identity information of the target course participant included in the target live course information.
In an alternative embodiment, the parsing of the target category of the target course participant identity information included in the target live course information described in step 2501 may specifically include technical contents described in steps 25011 to 25015.
Step 25011, determining the category of the identity information of the target course participant based on the live course information identification policy.
Step 25012, determining a first course knowledge point evaluation of the live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is an individual object, and determining that the target category is an active individual object on the premise that the first course knowledge point evaluation is greater than or equal to a third result description value.
In this embodiment, the individual object may be individual course participant identity information, and the course knowledge point evaluation may be a result of the course knowledge point evaluation performed on the target course participant identity information after live course information learning. An active individual object may be an object of the individual objects that has relatively more interactive activity.
Step 25013, determining that the target category is an inactive individual subject on the premise that the first course knowledge point score is less than the third result description value. In this embodiment, the inactive individual object may be an object having relatively less interactive activity among the individual objects.
Step 25014, determining a second course knowledge point evaluation of the live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is a group object, and determining that the target category is an active group object on the premise that the second course knowledge point evaluation is greater than or equal to a fourth result description value.
In this embodiment, the group object may be course participant identity information in which a plurality of individual objects are combined into a group, and the active group object may be an object having relatively more interactive activities in the group object.
Step 25015, on the premise that the second course knowledge point evaluation is smaller than the fourth result description value, determining that the target category is an inactive group object.
In this embodiment, the active community object may be an object having relatively few interactive activities in the community object.
Step 2502, determining the attention of the live course optimization content information.
In an alternative embodiment, the determining of the attention degree of the live course optimized content information in step 2502 may specifically include the content described in steps 25021 to 25024.
Step 25021, performing a cleaning process on the live-broadcast course optimized content information based on a set cleaning mode to determine a first course content change condition of the live-broadcast course optimized content information in a first classroom interaction state and a gate live-broadcast course information of the live-broadcast course optimized content information.
In this embodiment, the cleaning manner may be set to filter and remove redundant data according to actual requirements. The first classroom interaction state can be a state in which a live lesson-optimized content information interaction process is ongoing. The first course content change condition may be state change information during the course of performing live course optimized content information interaction. The biased live lesson information may be non-critical/marginal lesson information in the live lesson optimization content information.
Step 25022, determining the weighted result of the change of the content of the target course included in the partially living course information, which is greater than the change of the content of all courses of the second result description value.
Step 25023, obtaining a content statistical result of the live course optimized content information.
Step 25024, determining the comparison result between the curriculum content variation weighting result and the first curriculum of the target curriculum content superposition result as the attention.
In this embodiment, the target course content overlapping result is a course content overlapping result of the content statistics result and the first reference content.
Step 2503, determining the target interaction situation based on the interaction heat, the target category, the attention, the second interaction situation and the third interaction situation.
In an alternative embodiment, the determining the target interaction condition based on the interaction heat, the target category, the attention degree, the second interaction condition, and the third interaction condition, which is described in step 2503, may specifically include the contents described in steps 25031 to 25033.
Step 25031, performing weighting processing on the attention, the second interaction situation, and the third interaction situation to obtain a first weighting processing result, and determining a second course comparison result between the first weighting processing result and a second reference content.
Step 25032, on the premise that the target category indicates that the category of the identity information of the target course participant is an active object, determining a course content superposition result of the interaction heat and the second course comparison result as the target interaction condition.
Step 25033, determining an evaluation score of the target course participant identity information on the premise that the target category indicates that the category of the target course participant identity information is a small object, and determining a course content superposition result of the evaluation score, the interaction heat and the second course comparison result as the target interaction condition.
In an alternative embodiment, the determining the evaluation score of the identity information of the target course participant in step 25033 may specifically include: on the premise that the target course participant identity information is determined to be an individual object, determining a third course comparison result of the evaluation of the target course participant identity information and third reference content obtained through identification, and processing the third course comparison result through a first big data algorithm to obtain the evaluation score; on the premise of determining that the target course participant identity information is a group object, acquiring a fourth course comparison result of the evaluation of a reference individual object and the evaluation of a reference group object, determining a fifth course comparison result of the evaluation of the target course participant identity information and fourth reference content, and processing a course content superposition result of the fourth course comparison result and the fifth course comparison result through a second big data algorithm to obtain the evaluation score.
Through the technical scheme described in steps 2501 to 2503, the interaction effect of the target live course information can be determined from multiple dimensions.
In an alternative embodiment, after determining the target interaction scenario of the live course information of the target course participant identity information based on the interaction heat and the first interaction scenario, which is described in step 250, the method may further include the technical solutions described in steps 310 to 330.
In step 310, other interaction conditions of other live course information are determined. And the other live course information and the target live course information are obtained after analyzing the interaction state of the same course and comprise the live course information of the identity information of the target course participant.
And 320, determining the interaction condition with the maximum corresponding quantization value from the other interaction conditions and the target interaction condition, and determining the live course information corresponding to the interaction condition with the maximum corresponding quantization value as second live course information.
And 330, determining the intention content and/or the requirement content of the course participant identity information included in the second live course information based on the second live course information.
In this embodiment, the intention content and/or the demand content may be demand information or trend information for the course participant to select appropriate live course information. Therefore, the second live course information is analyzed, the actual requirement information of the course participant can be analyzed, and the live course can be selected in a targeted manner according to the requirement of the course participant.
In summary, after the target live-broadcast course information is acquired, a first interaction condition of the target live-broadcast course information is determined, course content optimization processing is performed on the first live-broadcast course information included in the target live-broadcast course information to obtain live-broadcast course optimized content information, an interaction heat of the live-broadcast course optimized content information is determined, and a target interaction condition of the live-broadcast course information of the identity information of the target course participant is determined according to the interaction heat and the first interaction condition. Because the course comparison result of the live course information of the identity information of the target course participant in the first live course information and the course of the first live course information is greater than the first result description value, the interactive heat of the live course information of the identity information of the target course participant can be accurately determined through the first live course information, the target interactive condition of the live course information of the identity information of the target course participant is comprehensively determined based on the interactive heat and the interactive condition of the target live course information, and the accuracy and the scene adaptability for determining the target interactive condition can be improved. Therefore, the problem of poor accuracy in identifying the classroom interaction condition of the live course information in the related technology can be solved, and the effect of accurately determining the classroom interaction condition of the live course information can be achieved
On the basis of the above, please refer to fig. 3, the present application also provides a block diagram of a big data processing device 40 for distance education, which comprises the following functional modules.
An information obtaining module 41, configured to obtain target live course information;
a live broadcast interaction module 42, configured to determine a first interaction condition of the target live broadcast course information;
a course optimization module 43, configured to perform course content optimization processing on first live-broadcast course information included in the target live-broadcast course information to obtain live-broadcast course optimized content information, where the first live-broadcast course information includes target course participant identity information, and a course comparison result between the live-broadcast course information of the target course participant identity information and the first live-broadcast course information is greater than a first result description value;
the heat determining module 44 is configured to determine an interaction heat of the live course optimized content information;
and the interaction analysis module 45 is configured to determine a target interaction condition of live course information of the identity information of the target course participant based on the interaction heat and the first interaction condition.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A big data processing method of distance education, comprising:
acquiring target live course information;
determining a first interaction condition of the target live course information;
performing course content optimization processing on first live-broadcast course information included in the target live-broadcast course information to obtain live-broadcast course optimized content information, wherein the first live-broadcast course information includes target course participant identity information, and a course comparison result of the live-broadcast course information of the target course participant identity information and the first live-broadcast course information is greater than a first result description value;
determining the interactive heat of the live course optimized content information;
and determining the target interaction condition of the live course information of the identity information of the target course participants based on the interaction heat and the first interaction condition.
2. The method of claim 1, wherein determining the first interaction with the target live curriculum information comprises:
determining a second interaction condition of the target live course information based on a live course information identification strategy;
analyzing live course information of the target course participant identity information using a first information analysis network to determine a third interaction condition of the live course information of the target course participant identity information, the first information analysis network being trained through machine learning using a plurality of sets of first training sample data, wherein each set of training sample data in the plurality of sets of first training sample data includes: live course information and the interaction condition of the live course information;
and determining the second interaction condition and the third interaction condition as the first interaction condition.
3. The method as claimed in claim 2, wherein determining the target interaction situation of the live course information of the target course participant identity information based on the interaction heat and the first interaction situation comprises:
analyzing the target category of the identity information of the target course participants included in the target live course information;
determining the attention of the live course optimization content information;
determining the target interaction situation based on the interaction heat, the target category, the attention, the second interaction situation and the third interaction situation.
4. The method of claim 3, wherein determining the interest level of the live-lesson-optimized content information comprises:
cleaning the live course optimized content information based on a set cleaning mode to determine a first course content change condition of the live course optimized content information in a first classroom interaction state and the biased live course information of the live course optimized content information;
determining the course content change weighting result of all course content change conditions of which the target course content change condition included in the partial live course information is larger than the second result description value;
acquiring a content statistical result of the live course optimized content information;
and determining a first course comparison result of the course content change weighting result and a target course content superposition result as the attention degree, wherein the target course content superposition result is a course content superposition result of the content statistical result and the first reference content.
5. The method as recited in claim 3, wherein parsing a target category of the target course participant identity information included in the target live course information comprises:
determining the category of the identity information of the target course participant based on a live course information identification strategy;
determining a first course knowledge point evaluation of live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is an individual object, and determining the target category as an active individual object on the premise that the first course knowledge point evaluation is greater than or equal to a third result description value;
determining the target category as an inactive individual subject on the premise that the first course knowledge point score is less than the third result description value;
determining a second course knowledge point evaluation of live course information of the target course participant identity information on the premise that the category of the target course participant identity information indicates that the target course participant identity information is a group object, and determining the target category as an active group object on the premise that the second course knowledge point evaluation is greater than or equal to a fourth result description value;
and on the premise that the evaluation of the second course knowledge point is smaller than the fourth result description value, determining that the target category is an inactive group object.
6. The method of claim 5, wherein determining the target interaction scenario based on the interaction heat, the target category, the attention, the second interaction scenario, and the third interaction scenario comprises:
weighting the attention degree, the second interaction condition and the third interaction condition to obtain a first weighting processing result, and determining a second course comparison result of the first weighting processing result and second reference content;
on the premise that the target class indicates that the class of the identity information of the target course participant is an active object, determining a course content superposition result of the interaction heat and the second course comparison result as the target interaction condition;
on the premise that the target class indicates that the class of the target course participant identity information is a small object, determining the evaluation score of the target course participant identity information, and determining the course content superposition result of the evaluation score, the interaction heat and the second course comparison result as the target interaction condition;
correspondingly, determining the evaluation score of the identity information of the target course participant comprises the following steps:
on the premise that the target course participant identity information is determined to be an individual object, determining a third course comparison result of the evaluation of the target course participant identity information and third reference content obtained through identification, and processing the third course comparison result through a first big data algorithm to obtain the evaluation score;
on the premise of determining that the target course participant identity information is a group object, acquiring a fourth course comparison result of the evaluation of a reference individual object and the evaluation of a reference group object, determining a fifth course comparison result of the evaluation of the target course participant identity information and fourth reference content, and processing a course content superposition result of the fourth course comparison result and the fifth course comparison result through a second big data algorithm to obtain the evaluation score.
7. The method as claimed in claim 1, wherein determining the interactive heat of the live lesson-optimized content information comprises:
obtaining a reference proportion index of the course participant identity information with the same category as the target course participant identity information;
determining a first proportion index of the identity information of the target course participant;
determining a first optimized value of a difference value between the reference ratio index and the first ratio index;
and determining a difference value between a fifth reference content and a sixth course comparison result as the interaction heat, wherein the sixth course comparison result is the course comparison result of the first optimized value and the reference proportion index.
8. The method as claimed in claim 1, wherein determining the interactive heat of the live lesson-optimized content information comprises:
determining two-classification partial live course information of the live course optimized content information in a second classroom interaction state;
acquiring the curriculum plate information of which the page click frequency is smaller than a fifth result description value and which is included in the two-classification partial live curriculum information based on a target adjustment strategy;
determining the curriculum plate information with the maximum page clicking frequency in the curriculum plate information as target curriculum plate information;
determining the page attention degree of the target course plate information based on a target big data algorithm;
determining a difference value between a sixth reference content and a seventh course comparison result as the interaction heat, wherein the seventh course comparison result is a course comparison result of the page attention degree of a target proportion value and a preset index;
correspondingly, after determining the target interaction condition of the live course information of the identity information of the target course participant based on the interaction heat and the first interaction condition, the method further comprises the following steps:
determining other interaction conditions of other live course information, wherein the other live course information and the target live course information are both obtained after analyzing the interaction state of the same course and comprise the live course information of the identity information of the target course participant;
determining the interaction condition with the maximum corresponding quantization value from the other interaction conditions and the target interaction condition, and determining the live course information corresponding to the interaction condition with the maximum corresponding quantization value as second live course information;
and determining the intention content and/or the requirement content of the course participant identity information included in the second live course information based on the second live course information.
9. A data processing server comprising a processor and a memory; the processor is connected in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 8.
10. A computer-readable storage medium, on which a program is stored which, when being executed by a processor, carries out the method of any one of the preceding claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115190324A (en) * | 2022-06-30 | 2022-10-14 | 广州市奥威亚电子科技有限公司 | Method, device and equipment for determining online and offline interactive live broadcast heat |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115190324A (en) * | 2022-06-30 | 2022-10-14 | 广州市奥威亚电子科技有限公司 | Method, device and equipment for determining online and offline interactive live broadcast heat |
| CN115190324B (en) * | 2022-06-30 | 2023-08-29 | 广州市奥威亚电子科技有限公司 | Method, device and equipment for determining online and offline interactive live broadcast heat |
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