CN114360586A - System and method for teaching psychological health education of college students - Google Patents
System and method for teaching psychological health education of college students Download PDFInfo
- Publication number
- CN114360586A CN114360586A CN202210029604.3A CN202210029604A CN114360586A CN 114360586 A CN114360586 A CN 114360586A CN 202210029604 A CN202210029604 A CN 202210029604A CN 114360586 A CN114360586 A CN 114360586A
- Authority
- CN
- China
- Prior art keywords
- voice
- taught
- reply
- machine learning
- learning model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Landscapes
- Electrically Operated Instructional Devices (AREA)
Abstract
The application discloses a system and a method for teaching psychological health education of college students, wherein the method comprises the following steps: acquiring attribute information of an object to be taught, wherein the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; selecting a character template for the object to be taught according to the attribute information; acquiring description information from the selected character template; converting the description information into voice to be played, and receiving the reply voice of the object to be taught; and determining whether the reply voice conforms to the psychological question corresponding to the virtual character or not according to the keywords in the reply voice. Through the method and the device, the problem that fine training cannot be carried out on actual language operation training in mental health training in the prior art is solved, so that the actual language operation training in the mental health can be carried out through a software system, the training efficiency is improved, and the actual training effect can be reasonably evaluated.
Description
Technical Field
The application relates to the field of education, in particular to a system and a method for teaching psychological health education of college students.
Background
College students have an increasing proportion of psychological problems, in which case more psychological knowledge is required of college staff.
At present, when the college staff are trained and educated, centralized training is generally relied on, which is helpful for knowledge mastering, but the tutoring of the psychological problem needs actual language assistance, and the actual language operation can not be well carried out in the centralized training.
For the above problems in the prior art, there is currently no good solution.
Disclosure of Invention
The embodiment of the application provides a system and a method for teaching psychological health education of college students, and the system and the method are used for at least solving the problem that the training on actual language operation in the psychological health training in the prior art cannot be well carried out.
According to an aspect of the present application, there is provided an university student mental health education teaching method including: acquiring attribute information of an object to be taught, wherein the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; obtaining description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; converting the description information into voice to be played, and receiving a reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information; and determining whether the reply voice conforms to the psychological question corresponding to the virtual character or not according to the keywords in the reply voice.
Further, converting the description information into voice for playing includes: inputting the description information into a first machine learning model, wherein the first machine learning model is obtained by using a plurality of groups of first training data for training, and each group of first training data in the plurality of groups of first training data comprises text information and voice corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
Further, determining whether the reply voice conforms to the psychological question corresponding to the virtual character according to the keyword in the reply voice includes: converting the reply speech to text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords.
Further, converting the response speech to text includes: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data; and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
According to another aspect of the present application, there is also provided an university student mental health education teaching system, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring attribute information of an object to be taught, and the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; the selection module is used for selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; the second acquisition module is used for acquiring description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; the conversion module is used for converting the description information into voice to be played and receiving reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information; and the determining module is used for determining whether the reply voice conforms to the psychological question corresponding to the virtual character according to the keywords in the reply voice.
Further, the conversion module is configured to: inputting the description information into a first machine learning model, wherein the first machine learning model is obtained by using a plurality of groups of first training data for training, and each group of first training data in the plurality of groups of first training data comprises text information and voice corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
Further, the determination module is to: converting the reply speech to text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords.
Further, the determination module is to: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data; and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
According to another aspect of the present application, there is also provided a memory for storing software for executing the above method.
According to another aspect of the application, there is also provided a processor for executing software for executing the above method.
In the embodiment of the application, the method comprises the steps of obtaining attribute information of an object to be taught, wherein the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; obtaining description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; converting the description information into voice to be played, and receiving a reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information; and determining whether the reply voice conforms to the psychological question corresponding to the virtual character or not according to the keywords in the reply voice. Through the method and the device, the problem that fine training cannot be carried out on actual language operation training in mental health training in the prior art is solved, so that the actual language operation training in the mental health can be carried out through a software system, the training efficiency is improved, and the actual training effect can be reasonably evaluated.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a college student mental health education teaching method according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
In this embodiment, a method for teaching university student mental health education is provided, and fig. 1 is a flowchart of a method for teaching university student mental health education according to an embodiment of the present application, as shown in fig. 1, and the steps related to fig. 1 are described below.
Step S102, obtaining attribute information of an object to be taught, wherein the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught;
step S104, selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template;
as an optional embodiment, if the gender of the virtual character is different from the gender of the object to be taught, the virtual character has a higher rank for the object to be taught than a virtual character having the same gender. Acquiring courses which are learned by the object to be taught, wherein the courses are from low-level virtual characters to high-level virtual characters, and the virtual characters at the level are set as finished courses under the condition that the description information reply voice in the character template corresponding to each virtual character conforms to the psychological problem of the virtual character; and selecting a character template corresponding to the virtual character with higher level corresponding to the learned course. If the number of virtual characters learned by the object to be taught exceeds a threshold value, selecting a virtual character with a higher level and the same gender as the object to be taught; and if the number of virtual characters learned by the object to be taught is less than or equal to a threshold value, selecting the virtual character with the gender different from that of the object to be taught.
Step S106, obtaining description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character;
step S108, converting the description information into voice to be played, and receiving a reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information;
for example, there are various ways to convert the description information into speech, for example, the description information is input into a first machine learning model, where the first machine learning model is trained by using multiple sets of first training data, and each set of the multiple sets of first training data includes text information and speech corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
Step S110, determining whether the reply voice conforms to the psychological question corresponding to the virtual character according to the keyword in the reply voice.
In this step, the reply speech may be converted into text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords.
Optionally, converting the reply speech to text comprises: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data; and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
Through the steps, the problem that good training cannot be carried out on actual language operation training in mental health training in the prior art is solved, so that the actual language operation training in the mental health can be carried out through a software system, the training efficiency is improved, and the actual training effect can be reasonably evaluated.
As an optional implementation manner, the description information includes a plurality of pieces of description information, a plurality of keywords are preconfigured for each piece of description information, and an order of occurrence of the plurality of keywords is preconfigured for the plurality of keywords; for each piece of description information, playing as a voice, for a reply voice of the piece of description information, determining whether the sequence of occurrence of keywords of a text corresponding to the reply voice coincides with the piece of description information, and determining a score of the reply voice according to the degree of coincidence, determining whether the reply passes for the piece of description information according to the score of the voice, playing a next piece of voice if the reply passes, and repeatedly playing the piece of voice if the reply does not pass until the score of the reply voice can pass.
As another alternative, determining the score of the response speech may determine a first score according to whether all the keywords pre-configured to appear in the text corresponding to the response speech are present, the higher the number of the keywords pre-configured to appear in the text corresponding to the response speech is, the higher the first score is; determining the appearance sequence of the text corresponding to the reply voice to determine a second score, wherein the second score is higher when the appearance sequence is the same as the appearance sequence of a plurality of keywords which are configured in advance; and obtaining the score of the reply voice according to the weighted sum of the first score and the second score. Optionally, the weights of the first score and the second score may be pre-configured by a user.
In another embodiment, in order to train the object to be taught better, in the case that the answer to the piece of description information is determined to pass according to the score of the answer voice, the piece of description information is repeatedly played again and again according to the preset times, the multiple answer voices of the object to be taught to the piece of description information are obtained, the voice intonation in each answer voice is obtained, the emotion change in the multiple answer voices is judged, and in the case that the emotion change meets the preset condition, the answer to the piece of description information is finally determined to pass. And prompting the object to be taught that obvious emotion change occurs in the answering process if the emotion change does not meet the preset condition.
Determining emotion from speech can be performed by a wide variety of methods, for example, a method of detecting an emotional state of a speech provider, the method comprising: acquiring a voice signal; dividing the speech signal into at least one of segments, frames, and subframes; extracting at least one acoustic feature from the speech signal; calculating statistical data from the at least one acoustic feature; classifying the speech as belonging to at least one emotional state using at least one neural network classifier; and storing in memory and outputting an indication of the at least one emotional state in a human recognizable format, wherein the speech is classified by a classifier taught to identify at least one emotional state from a limited number of emotional states; and wherein the speech is classified as emotional or non-emotional. Wherein the speech is classified as at least one of angry, sad, happy, afraid, and neutral. The at least one neural network is taught to recognize an emotional state by dividing speech samples into training and testing segments, and wherein an algorithm for recognizing an emotional state is adapted by comparing the classification from the neural network with a classification of at least one person.
The technical solution provided in this embodiment may be applied to a system, which is referred to as an interactive mental health education system, and the system includes a course list, a video player, and a plurality of video files, where the course list is provided with a play link pointing to each video file, and the video player plays the corresponding video file according to a play link request, and further includes a play monitoring module and a testing module, where the video files include an open segment file and a plurality of branch scene segment files, and the play link on the course list points to each open segment file; the playing monitoring module monitors the playing progress of the video file and starts the testing module after the playing of the current opening segment file or the branch scene segment file is finished; the test module displays test problems, options to be selected and selection frames to be selected in a pop-up window mode, the selection frames to be selected are used for a user to select the options to be selected, and each selection frame to be selected is provided with a link pointing to each branch scene segment file or each opening segment file; and the video player plays the corresponding open scene segment file or branch scene segment file according to the link.
Preferably, the video file is displayed in the form of animation or a real person real shooting short story, and the video player hides the video progress bar. The test questions comprise general questions and key questions, the general questions do not specify the most appropriate answer to be selected, and according to the to-be-selected items selected by the user, the test module plays the next branch scene segment file pointed by the to-be-selected item selection box; the key problem is provided with a designated most suitable option to be selected and an inappropriate option to be selected, and the designated most suitable option to be selected selection box is provided with a link pointing to the next branch scene segment file; and if the test module is not suitable for the option, popping up the key test question, the option to be selected and the option selection frame to be selected again by the test module according to the unsuitable option selected by the user. After the user selects the option to be selected, the test module further pops up a feedback popup window in the form of characters, voice or video. In a key problem, after the user selects an inappropriate option for three times continuously, the video player plays the opening clip file of the video file.
Preferably, the system may further include an identity recognition module, which provides two user identities of a student and a teacher, and performs identity verification before the user logs in, wherein the student identity is associated with and entered with information of a year and a class, and is associated with the corresponding teacher identity according to the class information. The student class management system further comprises a course management module, different course lists are arranged in different grades, when a student user logs in, the course management module extracts grade information of the student class management module and unlocks access links of video files corresponding to the course lists, and access of the video files corresponding to the course lists in the other grades is forbidden.
And each video file on the course list of each grade is provided with an access sequence, the system defaults to unlock the video file of the first sequence position, and after the video file of the previous sequence position is played, the course management module unlocks the video file of the next sequence position. After the student user finishes playing all the video files corresponding to the course lists of the current grade, the course management module unlocks the video files corresponding to all the course lists of the next grade.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
Such an apparatus or system is provided in this embodiment. The system is called a college student mental health education teaching system and comprises: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring attribute information of an object to be taught, and the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; the selection module is used for selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; the second acquisition module is used for acquiring description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; the conversion module is used for converting the description information into voice to be played and receiving reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information; and the determining module is used for determining whether the reply voice conforms to the psychological question corresponding to the virtual character according to the keywords in the reply voice.
The system or the apparatus is used for implementing the functions of the method in the foregoing embodiments, and each module in the system or the apparatus corresponds to each step in the method, which has been described in the method and is not described herein again.
For example, the conversion module is configured to: inputting the description information into a first machine learning model, wherein the first machine learning model is obtained by using a plurality of groups of first training data for training, and each group of first training data in the plurality of groups of first training data comprises text information and voice corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
For another example, the determination module is configured to: converting the reply speech to text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords. Optionally, the determining module is configured to: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data; and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
The problem that good training cannot be carried out on actual language operation training in mental health training in the prior art is solved through the embodiment, so that the actual language operation training in the mental health can be carried out through a software system, the training efficiency is improved, and the actual training effect can be reasonably evaluated.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A college student mental health education teaching method is characterized by comprising the following steps: acquiring attribute information of an object to be taught, wherein the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; obtaining description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; converting the description information into voice to be played, and receiving a reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information;
and determining whether the reply voice conforms to the psychological question corresponding to the virtual character or not according to the keywords in the reply voice.
2. The method of claim 1, wherein converting the description information into speech for playback comprises:
inputting the description information into a first machine learning model, wherein the first machine learning model is obtained by using a plurality of groups of first training data for training, and each group of first training data in the plurality of groups of first training data comprises text information and voice corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
3. The method according to claim 1, wherein determining whether the response speech matches the psychological question corresponding to the virtual character based on the keyword in the response speech includes: converting the reply speech to text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords.
4. The method of claim 3, wherein converting the response speech to text comprises: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data; and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
5. An university student mental health education teaching system, comprising: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring attribute information of an object to be taught, and the attribute information at least comprises the gender of the object to be taught and the learning progress of the object to be taught; the selection module is used for selecting a character template for the object to be taught according to the attribute information, wherein a plurality of character templates are configured in advance, each character template corresponds to a grade, and the grade is used for indicating the severity of psychological problems of a virtual character corresponding to the character template; the second acquisition module is used for acquiring description information from the selected character template, wherein the description information is used for describing psychological problems of the virtual character; the conversion module is used for converting the description information into voice to be played and receiving reply voice of the object to be taught, wherein the reply voice is a reply of the object to be taught to the voice converted from the description information; and the determining module is used for determining whether the reply voice conforms to the psychological question corresponding to the virtual character according to the keywords in the reply voice.
6. The system of claim 5, wherein the conversion module is configured to: inputting the description information into a first machine learning model, wherein the first machine learning model is obtained by using a plurality of groups of first training data for training, and each group of first training data in the plurality of groups of first training data comprises text information and voice corresponding to the text information; and acquiring the voice output by the first machine learning model from the first machine learning model.
7. The system of claim 5, wherein the determination module is configured to: converting the reply speech to text; searching keywords from the text obtained after conversion; and determining whether the reply voice conforms to the psychological problem corresponding to the virtual character or not according to the searched keywords.
8. The system of claim 7, wherein the determination module is configured to: inputting the reply speech into a second machine learning model, wherein the second machine learning model is trained using a plurality of sets of second training data, and each set of the plurality of sets of second training data includes: voice data and a text corresponding to the voice data;
and acquiring text corresponding to the reply voice output by the second machine learning model from the second machine learning model.
9. Memory for storing software, characterized in that the software is adapted to run the method of any of claims 1 to 4.
10. A processor for executing software, characterized in that the software is adapted to run the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210029604.3A CN114360586A (en) | 2022-01-12 | 2022-01-12 | System and method for teaching psychological health education of college students |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210029604.3A CN114360586A (en) | 2022-01-12 | 2022-01-12 | System and method for teaching psychological health education of college students |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114360586A true CN114360586A (en) | 2022-04-15 |
Family
ID=81109367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210029604.3A Withdrawn CN114360586A (en) | 2022-01-12 | 2022-01-12 | System and method for teaching psychological health education of college students |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114360586A (en) |
-
2022
- 2022-01-12 CN CN202210029604.3A patent/CN114360586A/en not_active Withdrawn
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200286396A1 (en) | Following teaching system having voice evaluation function | |
| CN112487139B (en) | Text-based automatic question setting method and device and computer equipment | |
| CN109360550A (en) | Test method, device, equipment and the storage medium of voice interactive system | |
| CN117149989B (en) | Training method for large language model, text processing method and device | |
| CN107133303A (en) | Method and apparatus for output information | |
| CN111681143A (en) | Multi-dimensional analysis method, device, equipment and storage medium based on classroom voice | |
| CN112989822B (en) | Method, device, electronic equipment and storage medium for recognizing sentence categories in conversation | |
| CN114218488A (en) | Information recommendation method and device based on multi-modal feature fusion and processor | |
| CN108710653B (en) | On-demand method, device and system for reading book | |
| CN110852071B (en) | Knowledge point detection method, device, equipment and readable storage medium | |
| CN113360598A (en) | Matching method and device based on artificial intelligence, electronic equipment and storage medium | |
| CN104700831B (en) | The method and apparatus for analyzing the phonetic feature of audio file | |
| CN111681678A (en) | Method, system, device and storage medium for automatically generating sound effect and matching video | |
| CN111897955B (en) | Comment generation method, device, equipment and storage medium based on encoding and decoding | |
| CN112015883A (en) | English question automatic generation method and system | |
| CN110516236B (en) | Social short text fine-grained emotion acquisition method | |
| CN114173191B (en) | Multi-language answering method and system based on artificial intelligence | |
| CN114360586A (en) | System and method for teaching psychological health education of college students | |
| CN112687296B (en) | Audio disfluency identification method, device, equipment and readable storage medium | |
| CN112347990B (en) | Multi-mode-based intelligent manuscript examining system and method | |
| CN114691903A (en) | Intelligent course testing method and system, electronic equipment and storage medium | |
| CN114822557A (en) | Method, device, equipment and storage medium for distinguishing different sounds in classroom | |
| CN113837167A (en) | Text image recognition method, device, equipment and storage medium | |
| US10453354B2 (en) | Automatically generated flash cards | |
| CN113705248B (en) | Method and device for processing tactical training data based on result evaluation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220415 |
|
| WW01 | Invention patent application withdrawn after publication |