Online Audio and Video Learning Method Based on Blackboard Model Collaboration

Abstract

The present invention discloses an online audio-visual learning method based on blackboard model collaboration. The present invention relates to the technical field of blackboard models, and solves the problem that the associated efficiency is too slow during the actual processing process and content output. The present invention classifies the text content associated with the index position, groups the content of the same type into one category, and sets a classification mark; this clear classification method helps to process and manage different types of content in a targeted manner, and provides convenience for subsequent type combination and index output; intelligent and efficient type combination: according to the relationship between the total number of index paths and the total number of type contents, intelligently determine whether to perform type combination, and select the best process by calculating the process variance; this optimized combination method can reasonably allocate resources, enabling different types of content to be efficiently processed under limited index paths, avoiding waste of resources, and improving the overall index efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of blackboard models, and specifically to an online audio-visual learning method based on blackboard model collaboration. Background Art

[0002] With the rapid development of the times, intelligent blackboard models have gradually emerged. According to the corresponding teacher's voice content during class, relevant text content is selected from the cloud database and output, facilitating corresponding teaching content, etc.

[0003] However, in the process of collaborative processing of intelligent blackboard models, not only does it need to perform real-time processing on the collected audio-visual content, but it also needs to select relevant content based on the processed feature text and output the selected content. In the actual processing process, the processing process is relatively slow, resulting in a too slow output efficiency when outputting content, and users cannot obtain a good user experience. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an online audio-visual learning method based on blackboard model collaboration, which solves the problem that the associated efficiency is too slow during the actual processing process and content output.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An online audio-visual learning method based on blackboard model collaboration includes the following steps:

[0006] Step 1: Obtain the audio signals generated during class, and generate specific audio feature texts based on the relevant features of the obtained audio signals. The specific sub-steps are as follows:

[0007] S11: Collect according to standard audio sampling parameters, with a sampling bit depth of 16 bits. Divide the collected single audio signal into short frames, so that a single audio signal generates several short frames and generates a short frame sequence;

[0008] S12: Extract features from each short frame in the short frame sequence. Based on the MFCC feature values associated with each short frame, confirm the audio comprehensive features of this audio signal:

[0009] S121: Perform FFT on each short frame to convert the time-domain signal into a frequency-domain signal and obtain the spectrum. The formula for FFT is: , where X[k] is the frequency-domain signal, x[n] is the time-domain signal, N is the length of the frame, where n is the index of the time-domain signal representing the time point, k is the index of the frequency-domain signal representing the frequency point, and j is the imaginary unit representing the complex exponential function;

[0010] S122. Then take the modulus square of the FFT result to obtain the power spectrum: P[k] = |X[k]| 2 ;

[0011] S123. Pass the power spectrum through a set of Mel filters to convert the linear frequency to Mel frequency. The conversion formula for Mel frequency is: , where f is the linear frequency and f mel is the Mel frequency;

[0012] S124. Then take the logarithm of the output of each Mel filter to obtain the log energy: , where H m [k] is the frequency response of the m-th Mel filter and E[m] is the log energy;

[0013] S125. Perform a discrete cosine transform on the log energy to obtain the MFCC coefficients. The formula for the discrete cosine transform is: , where c[n] is the n-th MFCC coefficient, M is the number of Mel filters. Take the mean of the first 12 - 13 MFCC coefficients to confirm the MFCC eigenvalue of this short frame;

[0014] S126. Apply the same processing steps as in steps S121 - S125 to each of the several short frames in the short frame sequence to sequentially confirm the MFCC eigenvalues of the corresponding short frames, and then sum the MFCC eigenvalues of the several short frames to lock the audio comprehensive feature of this audio signal;

[0015] S13. Based on the audio comprehensive feature associated with the corresponding audio signal, compare this audio signal with a preset feature check table to confirm the check character associated with this audio signal. The feature check table is a preset table, and sort the check characters associated with the continuously incoming audio signals to generate the audio feature text at this stage, where the interval time between continuously incoming audio signals is less than 1 second;

[0016] Step Two. Compare the audio feature text with the cloud database for character check, select the check text from the cloud database, and then confirm the position of the check text. Mark the confirmed position as the index position. The specific sub - steps are:

[0017] S21. Designate the confirmed audio feature text as the main text, confirm the text titles of other verification texts from the cloud database, use the confirmed text titles as secondary texts, and select similar texts from the determined several groups of secondary texts: Compare the characters of the secondary text with the main text, designate the same characters as the same type of characters, and record the total number G of the same type of characters. Then, designate the total number of characters of the secondary text as H1 and the total number of characters of the main text as H2. Use ZB1 = G÷H1 and ZB2 = G÷H2 to confirm the proportion ZB1 and ZB2 of the same type of characters, and then lock the average proportion based on ZB1 and ZB2. Designate the secondary text with an average proportion ≥ 60% as the similar text;

[0018] S22. Confirm the storage location of the similar text from the cloud database, and designate the confirmed storage location as the index location;

[0019] Step 3. Based on the confirmed index location and the preset index path, classify the text content associated with the index location, confirm multiple groups of type content, and perform type combination on the multiple groups of type content according to the total number of index paths to confirm the combined content, and integrate multiple combined contents into the content group to be indexed. The specific method is as follows:

[0020] S31. Based on the text content associated with the index location, confirm the different types included in the corresponding text content, classify the content of different types, classify the content of the same type into the same category, lock multiple groups of type content of different types, and set classification marks during the classification process;

[0021] S32. Confirm the total number of preset index paths and designate it as Zs, and then confirm the total number of type content of different types and designate it as ZG. If Zs≥ZG, there is no need to perform type combination. Directly designate the corresponding type content as the combined content, and integrate multiple combined contents into the content group to be indexed. If Zs<ZG, randomly combine the type content of multiple groups of different types and continue several combination processes. In each combination process, the total number of multiple groups of combined content is always the same as ZG. Select the best process from each combination process. The specific method is as follows:

[0022] S321. Confirm the data capacity of different combined content in each combination process, and designate the confirmed different data capacities as R i , where i represents different combined content, and then confirm the multiple groups of data capacities R i Perform variance processing on them to lock the process variance of this combination process;

[0023] S322. Sequentially confirm the process variances associated with each different combination process, select the smallest process variance from the confirmed different process variances, and label the combination process associated with the smallest process variance as the optimal process;

[0024] S33. Label the multiple groups of combined contents associated with the optimal process as the content group to be indexed;

[0025] Step Four. According to the labeled content group to be indexed, index and output the combined contents inside the content group to be indexed according to the preset indexing path. Based on the indexing features generated during the indexing process, determine the combined content that is most suitable for indexing and output on the corresponding indexing path, label the determined combined content as the execution content, and perform indexing and output. The specific sub-steps are as follows:

[0026] S41. Based on the total number Zs of indexing paths, select part of the content from a single combined content, evenly divide the selected part of the content into Zs sub-indexed contents, sequentially allocate the Zs sub-indexed contents to the associated indexing paths, output the content through the associated indexing paths, record multiple groups of indexing rates of the corresponding indexing paths, perform mean processing on the multiple groups of indexing rates, confirm the average indexing speed of the corresponding indexing path, and label it as SY o , where o represents different indexing paths. Regarding this combined content, select the indexing path associated with SY o max as the execution path of this combined content, label this combined content as the execution content, and index and output the execution content according to this execution path;

[0027] S42. Whenever there is a group of execution paths in its indexing path, reduce its total number by a value of 1, and use the same method as in step S41 to process the remaining combined contents, confirm the execution paths associated with the corresponding combined contents and the determined execution contents, and index and output the determined execution contents according to the determined execution paths;

[0028] According to this processing method, sequentially process different combined contents, perform content indexing and fast output. The multiple groups of combined contents after indexing are reorganized according to the set division marks, the text content is confirmed, and the confirmed text content is directly displayed on the blackboard model.

[0029] The present invention provides an online audio-visual learning method based on blackboard model collaboration. Compared with the prior art, it has the following beneficial effects:

[0030] In the processing of the class audio signal, by segmenting the audio into short frames and extracting MFCC feature values, the time-domain and frequency-domain characteristics of the audio are fully considered, simulating the human ear's perception of sound. This delicate processing method can accurately capture the characteristics of the audio signal, providing a solid foundation for subsequent text matching, making the associated text of the locked corresponding voice content more accurate, and helping learners accurately obtain the key information of the course;

[0031] When comparing the audio feature text with the cloud database, by calculating the ratio of similar characters and determining the average ratio, similar texts are screened out; this scientific screening method can quickly locate the content highly relevant to the audio feature text from a large number of texts, reducing unnecessary search time and improving the efficiency of data matching;

[0032] Classify the text content associated with the index position, group the same type of content into one category, and set classification marks; this clear classification method helps to process and manage different types of content in a targeted manner, providing convenience for subsequent type combination and index output; Intelligent and efficient type combination: According to the relationship between the total number of index paths and the total number of type contents, intelligently determine whether to perform type combination, and select the best process by calculating the process variance; this optimized combination method can reasonably allocate resources, enabling different types of content to be efficiently processed under limited index paths, avoiding waste of resources, and improving the overall index efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] Please refer to Figure 1 , this application provides an online audio-visual learning method based on blackboard model collaboration, including the following steps:

[0036] Step 1: Obtain the audio signal generated during the class, and generate specific audio feature text based on the relevant features of the obtained audio signal. Specifically, different audio is associated with different audio features, and then based on different audio features and the preset associated text, lock the specific text associated with the corresponding audio feature. The specific sub-steps of generating specific audio feature text are as follows:

[0037] S11. Collect according to standard audio sampling parameters. Generally, the sampling frequency is set to 16 kHz or 44.1 kHz, and the sampling bit depth is 16 bits to ensure audio quality. Split the collected single audio signal into short frames, with the duration of each frame usually being 20 - 30 milliseconds. For an audio with a sampling frequency of 16 kHz, each frame contains 320 - 480 sampling points, so that a single audio signal generates several short frames and a short frame sequence is generated.

[0038] S12. Extract features for each short frame in the short frame sequence. Based on the MFCC feature values associated with each short frame, confirm the comprehensive audio features of this audio signal:

[0039] S121. Perform FFT on each short frame to convert the time-domain signal into a frequency-domain signal and obtain the spectrum. The formula for FFT is: , where X[k] is the frequency-domain signal, x[n] is the time-domain signal, N is the length of the frame, where n is the index of the time-domain signal representing the time point, k is the index of the frequency-domain signal representing the frequency point, and j is the imaginary unit representing the complex exponential function;

[0040] S122. Then take the modulus square of the result of FFT to obtain the power spectrum: P[k] = |X[k]| 2 ;

[0041] S123. Pass the power spectrum through a set of Mel filters to convert the linear frequency to the Mel frequency, which is more in line with the human ear's perception characteristics of sound. The conversion formula for the Mel frequency is: , where f is the linear frequency, f mel is the Mel frequency. Its Mel filter bank usually consists of 20 - 40 triangular filters, and the center frequencies of each filter are evenly distributed according to the Mel frequency;

[0042] S124. Then take the logarithm of the output of each Mel filter to obtain the log energy: , where H m [k] is the frequency response of the m-th Mel filter, and E[m] is the log energy;

[0043] S125. Perform discrete cosine transform on the log energy to obtain the MFCC coefficients. The formula for discrete cosine transform is: , where c[n] is the n-th MFCC coefficient, M is the number of Mel filters. Here, take the first 12 - 13 MFCC coefficients as features because these coefficients contain the main spectral characteristics of the audio signal. Perform mean processing on the first 12 - 13 MFCC coefficients to confirm the MFCC feature values of this short frame;

[0044] S126. Apply the same processing steps as in steps S121 - S125 to each of the several short frames in the short frame sequence to confirm the MFCC feature values of the corresponding short frames in turn. Then sum up the MFCC feature values of the several short frames to lock the audio comprehensive feature of this audio signal;

[0045] S13. Based on the audio comprehensive feature associated with the corresponding audio signal, compare this audio signal with a preset feature check list to confirm the check character associated with this audio signal. The feature check list is a preset table, formulated in advance by relevant operators based on experience, and sort the check characters associated with continuous audio signals to generate the audio feature text at this stage, where the interval time between continuous audio signals is less than 1 second;

[0046] Specifically, by using the above - mentioned specific content and related features, the specific text associated with the corresponding voice content can be locked, and then content indexing can be performed based on the corresponding text to quickly display the content, so as to achieve a better content output effect;

[0047] Step two. Compare the characters of this audio feature text with the cloud database, select the check text from the cloud database, and then confirm the position of the check text. Mark the confirmed position as the index position. Specifically, each different check text has a different storage path node, and different storage path nodes correspond to different storage positions. Subsequently, during the indexing process, directly index these storage positions. The specific sub - steps for marking the index position are as follows:

[0048] S21. Mark the confirmed audio feature text as the main text, and confirm the text titles of other check texts from the cloud database. Use the confirmed text titles as secondary texts, and select the similarity text from the determined several groups of secondary texts: Compare the characters of the secondary text with the main text, mark the same characters as the same - type characters, and record the total number G of the same - type characters. Then mark the total number of characters of the secondary text as H1 and the total number of characters of the main text as H2. Use ZB1 = G÷H1 and ZB2 = G÷H2 to confirm the proportion ZB1 and ZB2 of the same - type characters, and then lock the average proportion based on ZB1 and ZB2. Mark the secondary text with an average proportion ≥60% as the similarity text;

[0049] S22. Confirm the storage position of the similarity text from the cloud database and mark the confirmed storage position as the index position;

[0050] Specifically, each different text content has a different storage position, and the specific content associated with the corresponding text is stored in different storage positions. Therefore, the specific content associated with the corresponding storage position can be indexed to index the corresponding content and ensure the specific indexing rate;

[0051] Step 3: Based on the confirmed index positions and the preset index paths, classify the text content associated with the index positions, confirm multiple groups of type content, and perform type combinations on the multiple groups of type content according to the total number of index paths, confirm the combined content, and integrate multiple combined contents into a content group to be indexed. The specific sub-steps for confirmation are as follows:

[0052] S31: Based on the text content associated with the index positions, confirm the different types included in the corresponding text content (different data types correspond to different data formats), classify the different types of content, classify the content of the same type into the same category, lock multiple groups of type content of different types, and set classification marks during the classification process (to facilitate subsequent combination of multiple groups of type content of different types according to the classification marks);

[0053] S32: Confirm the total number of preset index paths and label it as Zs, then confirm the total number of type content of different types and label it as ZG. If Zs ≥ ZG, there is no need to perform type combination, directly label the corresponding type content as combined content, and integrate multiple combined contents into a content group to be indexed (that is, when the total number of index paths exceeds the total number of type content, in this case, directly index and output the type content of different types according to different index paths without performing type combination). If Zs < ZG, randomly combine multiple groups of type content of different types and continue for several combination processes. In each combination process, the total number of multiple groups of combined content is always consistent with ZG. Select the best process from each combination process:

[0054] S321: Confirm the data capacity of different combined content in each combination process, and label the confirmed different data capacities as R i , where i represents different combined content, and then perform variance processing on the confirmed multiple groups of data capacities R i to lock the process variance for this combination process;

[0055] S322: Confirm the process variances associated with each different combination process in turn, and select the smallest process variance from the confirmed different process variances. Label the combination process associated with the smallest process variance as the best process. Specifically, assume i = 1, 2,..., q, where q represents the total number of combined content, and then perform mean processing on the confirmed multiple groups of data capacities to confirm the mean capacity Jz, confirm the process variances associated with the corresponding multiple groups of data capacities;

[0056] S33: Label the multiple groups of combined content associated with the best process as the content group to be indexed;

[0057] Step 4. According to the calibrated content groups to be indexed, index and output the combined content within the content groups to be indexed according to the preset index paths. Based on the index features generated during the indexing process, determine the combined content that is most suitable for index output for the corresponding index path, calibrate the determined combined content as the execution content, and perform index output. The specific sub-steps for determining the execution content are as follows:

[0058] S41. Based on the total number Zs of index paths, select part of the content from a single combined content, evenly divide the selected part of the content into Zs sub-indexed contents, sequentially allocate the Zs sub-indexed contents to the associated index paths, output the content through the associated index paths, record multiple groups of index rates for the corresponding index paths, perform an average process on the multiple groups of index rates, confirm the average index rate of the corresponding index path, and calibrate it as SY o , where o represents different index paths. Regarding this combined content, select the index path associated with SY o max as the execution path of this combined content, calibrate this combined content as the execution content, and index and output the execution content according to this execution path;

[0059] S42. Whenever there is a group of execution paths in its index path, reduce its total number by 1, and use the same method as in step S41 to process the remaining combined content, confirm the execution path associated with the corresponding combined content and the determined execution content, and index and output the determined execution content according to the determined execution path;

[0060] According to this processing method, process different combined contents in sequence, perform content indexing, and perform fast output to ensure the output rate;

[0061] Specifically, it is assumed that there are three groups of contents in the content group to be indexed, corresponding to three groups of preset index paths. For a single group of contents among the three groups of contents, select the content of the single group to complete the specific determination of part of the content, then divide the determined part of the content, divide such part of the content into three sub-indexed contents, and then index and output the divided different sub-indexed contents according to different index paths. Each different index path will generate different index rates. Select the maximum value from the generated different index rates, that is, the group of index paths with the fastest index rate, and index and output the corresponding combined content according to the selected index path to ensure the corresponding index rate;

[0062] Subsequently, different combined contents are associated with different index paths, so that each combined content can reach the best index state, so as to achieve a faster index effect. The relevant content after indexing will be directly displayed on the blackboard model for the corresponding teacher to select.

[0063] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0064] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An online audio-visual learning method based on blackboard model collaboration, characterized in that, It includes the following steps: Step 1: Obtain the audio signals generated during the class, and generate specific audio feature texts based on the relevant features of the obtained audio signals. The specific sub-steps are as follows: S11: Collect according to the standard audio sampling parameters, with a sampling bit depth of 16 bits. Split the collected single audio signal into short frames, so that a single audio signal generates several short frames, and generate a short frame sequence; S12: Extract features from each short frame in the short frame sequence. Based on the MFCC feature values associated with each short frame, confirm the audio comprehensive features of this audio signal; S13: Based on the audio comprehensive features associated with the corresponding audio signal, compare this audio signal with a preset feature check list to confirm the check characters associated with this audio signal. The feature check list is a preset list, and sort the check characters associated with the continuous audio signals to generate an audio feature text, where the interval time between the continuous audio signals is less than 1 second; Step 2: Compare the audio feature text with the cloud database for character check, select the check text from the cloud database, and then confirm the position of the check text, and mark the confirmed position as the index position; Step 3: Based on the confirmed index position and the preset index path, classify the text content associated with the index position into multiple groups of type content, confirm the combined content according to the total number of index paths, and integrate multiple combined contents into a group of content to be indexed; Step 4: According to the marked group of content to be indexed, index and output the combined content inside the group of content to be indexed according to the preset index path. Based on the index features generated during the indexing process, determine the combined content that is most suitable for index output for the corresponding index path, mark the determined combined content as the execution content, and perform index output.

2. The online audio-visual learning method based on blackboard model collaboration according to claim 1, wherein, In the step S12, confirm the audio comprehensive features of this audio signal: S121. Perform FFT on each short frame to convert the time-domain signal into a frequency-domain signal and obtain a spectrum. The formula for FFT is as follows: , where X[k] is the frequency-domain signal, x[n] is the time-domain signal, N is the length of the frame, n is the index of the time-domain signal representing the time point, k is the index of the frequency-domain signal representing the frequency point, and j is the imaginary unit representing the complex exponential function; S122. Then take the modulus square of the FFT result to obtain the power spectrum: P[k] = |X[k]| 2 ; S123. Pass the power spectrum through a set of Mel filters to convert the linear frequency to the Mel frequency. The conversion formula for the Mel frequency is: , where f is the linear frequency and f mel is the Mel frequency; S124. Then take the logarithm of the output of each Mel filter to obtain the log energy: , where H m [k] is the frequency response of the m-th Mel filter, and E[m] is the log energy; S125. Perform a discrete cosine transform on the logarithmic energy to obtain MFCC coefficients. The formula for the discrete cosine transform is: where c[n] is the nth MFCC coefficient, M is the number of Mel filters. Perform a mean processing on the first 12 - 13 MFCC coefficients to confirm the MFCC eigenvalue of this short frame; S126: Apply the same processing steps as in steps S121 - S125 to each of the several short frames existing in the short frame sequence to confirm the MFCC feature values of the corresponding short frames in turn, and then sum up the MFCC feature values of the several short frames to lock the audio comprehensive features of this audio signal.

3. The audio and video online learning method based on blackboard model collaboration according to claim 1, wherein In the step 2, the specific sub-steps for marking the index position are as follows: S21: Mark the confirmed audio feature text as the main text, and confirm the text titles of other check texts from the cloud database. Use the confirmed text titles as the secondary texts, and select the similarity texts from the determined several groups of secondary texts: Compare the secondary texts with the main text for character check, mark the same characters as the same type of characters, and record the total number G of the same type of characters. Then, mark the total number of characters of the secondary text as H1 and the total number of characters of the main text as H2. Use ZB1 = G÷H1 and ZB2 = G÷H2 to confirm the proportion ZB1 and ZB2 of the same type of characters, and then lock the average proportion based on ZB1 and ZB2. Mark the secondary text with an average proportion ≥ 60% as the similarity text; S22. Confirm the storage location of the similarity text in the cloud database, and mark the confirmed storage location as the index location.

4. The online audio-visual learning method based on blackboard model collaboration according to claim 1, characterized in that In step 3, the specific way to confirm the combined content is as follows: S31. Based on the text content associated with the index location, confirm the different types included in the corresponding text content, divide the content of different types, classify the content of the same type into the same category, lock multiple groups of type content of different types, and set division marks during the division process. S32. Confirm the total number of preset index paths and mark it as Zs, then confirm the total number of type content of different types and mark it as ZG. If Zs≥ZG, there is no need to perform type combination. Directly mark the corresponding type content as the combined content, and integrate multiple combined contents into the content group to be indexed.

5. The online audio-visual learning method based on blackboard model collaboration according to claim 4, characterized in that, In step S32, if Zs<ZG, randomly combine the multiple groups of type content of different types and continue several combination processes. In each combination process, the total number of multiple groups of combined content is always the same as ZG. Select the best process from each combination process. S33. Mark the multiple groups of combined content associated with the best process as the content group to be indexed.

6. The online audio-visual learning method based on blackboard model collaboration according to claim 5, characterized in that In step S32, the specific way to select the best process is as follows: S321. Confirm the data capacity of different combination contents in each combination process, and label the confirmed different data capacities as R i , where i represents different combination contents, and then perform variance processing on the confirmed multiple groups of data capacities R i to lock the process variance regarding this combination process; S322. Confirm the process variances associated with each different combination process in turn, and select the smallest process variance from the confirmed different process variances. Mark the combination process associated with the smallest process variance as the best process.

7. The audio and video online learning method based on blackboard model collaboration according to claim 1, wherein In step 4, the specific sub-steps to confirm the execution content are as follows: S41. Based on the total number Zs of index paths, select partial content from a single combined content, evenly divide the selected partial content into Zs sub-indexed contents, sequentially allocate the Zs sub-indexed contents to the associated index paths, output the content through the associated index paths, record multiple groups of indexing rates for the corresponding index paths, perform mean processing on the multiple groups of indexing rates, confirm the average indexing speed of the corresponding index paths, and calibrate it as SY o , where o represents different index paths. For this combined content, each different index path will generate different indexing rates. From the generated different average indexing speeds SY o , select the maximum value SY o max, and use it as the execution path for this combined content. Index and output the execution content according to this execution path; S42. When there is a group of execution paths in its index path, reduce its total number by 1, and use the same method as in step S41 to process the remaining combined content. Confirm the execution path associated with the corresponding combined content and the determined execution content, and index and output the determined execution content according to the determined execution path. According to this processing method, process different combined contents in turn, perform content indexing, and perform fast output. The multiple groups of combined content after indexing are reorganized according to the set division marks, the text content is confirmed, and the confirmed text content is directly displayed on the blackboard model.

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