Method and device for adjusting course information based on natural language processing

Abstract

The invention discloses a course information adjusting method and device based on natural language processing, and belongs to the technical field of course information adjustment, and the method comprises the steps: obtaining the coherence importance of any pair of previous course data packet queues in each storage time point according to the numerical dispersion amplitude of any pair of previous course data packet queues; obtaining the numerical coherence degree of all course data packet queues of any pair of types; obtaining numerical gentle amplitudes of all course data packet queues of various types; obtaining a last group of the latest course data packet queue and all types of all course data packet queues by using all action amplitudes; according to the method, the coherence degree between the previous course data packets of different types and the action condition of the recent course data packets on the previous course data packets are involved, the groups accurately reflecting approximate numerical value arrangement are obtained, and therefore the accurate course data packet storage effect is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of course information adjustment, and in particular relates to a course information adjustment method and device based on natural language processing. Background Art

[0002] The teaching completion degree is the completion progress of the teaching plan. At present, it is often necessary to use the existing technical solutions such as the patent publication number "CN118279105A" to process course information based on natural language until the teaching completion degree corresponding to the course is formed. The method of processing course information based on natural language is to form a course data packet and store it in a database, and obtain a recognition model through a natural language model and other models (natural language models and other models include training vector machines, natural language models and image models). The course data packet in the database is recognized to obtain a corresponding completion state vector. The course data packet contains manually organized courses and corresponding teaching images, teaching audio, and classroom personnel coordinates.

[0003] To improve the storage efficiency when forming course data packages and storing them in the database, encoding and reducing the storage of several types of course data packages through encoding methods such as the DEFLATE method can greatly improve the storage efficiency and reduce the storage load of the database.

[0004] In order to improve the efficiency of course data packet encoding, several types of course data packets formed by the grouping method are often grouped and cut, and the groups are encoded and stored in reduced quantities. Currently, when a newly formed recent course data packet (that is, a newly formed course data packet) is added to a group composed of existing past course data packets (that is, previously stored course data packets), the effect of the recent course data packet on the past course data packets is not taken into account, which often damages the existing groups and destroys the course data packets, making the encoded data incomplete, and thus failing to achieve accurate course data packet storage effects. Summary of the invention

[0005] In order to solve the defects in the prior art, the present invention proposes a method and device for adjusting course information based on natural language processing, which obtains the past course data packet queues and the latest course data packet queues to be stored at each storage time point in the past period; according to the numerical dispersion amplitude of any pair of types of past course data packet queues, the coherent importance of any pair of types of past course data packet queues at each storage time point is obtained, which reflects the stability of the data of any pair of types of past course data packet queues at each storage time point; the numerical fluctuation status difference of any pair of past course data packet queues at each storage time point is adjusted through the coherent importance to obtain the full value of any pair of types. The numerical coherence of the whole course data packet queue; obtaining the numerical flat amplitude of the whole course data packet queue of each type; obtaining the total effect amplitude of the latest course data packet queue of all types on the whole course data packet queue of all types, and using the total effect amplitude to obtain the final grouping of the latest course data packet queue and the whole course data packet queue of all types; encoding all the latest course data packets according to the final grouping, the present invention involves the coherence between the past course data packets of different types and the effect of the latest course data packet on the past course data packets, and obtaining the grouping that accurately reflects the approximate numerical arrangement, so as to obtain the accurate course data packet storage effect.

[0006] The present invention uses the following technical solutions.

[0007] A method for adjusting course information based on natural language processing, comprising: Processing the course information based on natural language until the teaching completion degree corresponding to the course is formed, wherein the method of processing the course information based on natural language includes forming a course data package and storing it in a database; During the process of forming the course data package and storing it in the database, it also includes: Step 1, obtain the latest course data packet queue of all types to be stored and the past course data packet queue of all types at each storage time point in the pre-set past period; the past course data packet queue of all types at all storage time points forms the whole course data packet queue of all types; Step 2, according to the numerical dispersion amplitude of the previous course data packet queues of any pair of types, until the numerical coherence of the course data packet queues of any pair of types is obtained; Step 3, according to the numerical correlation between the whole course data packet queue of each type and the whole course data packet queue of each other type, until the whole effect amplitude of the nearest course data packet queue of all types on the whole course data packet queue of all types is obtained; Step 4, obtain the final grouping of the latest course data packet queue and all course data packet queues of all types according to the total effect amplitude; encode and store all the latest course data packets according to the final grouping.

[0008] Furthermore, in Step 1, the text information in the obtained course data package is transformed into an attribute vector through the Word2Vec model, and the obtained entire attribute vector is processed by the ridge regression method; In Step 2, the method of obtaining the numerical coherence of a random pair of types of course data packet queues according to the numerical dispersion amplitude of a random pair of types of past course data packet queues includes: obtaining the coherence importance of a random pair of types of past course data packet queues at each storage time point according to the numerical dispersion amplitude of a random pair of types of past course data packet queues; obtaining the numerical coherence of a random pair of types of course data packet queues according to the numerical fluctuation state distinction and coherence importance of a random pair of types of past course data packet queues at each storage time point; In Step 3, the method of obtaining the overall effect amplitude of the latest course data packet queue of all types on all course data packet queues of all types according to the numerical coherence between all course data packet queues of all types and all course data packet queues of all other types, includes: obtaining the numerical smooth amplitude of all course data packet queues of all types according to the numerical coherence between all course data packet queues of all types and all course data packet queues of all other types, as well as the numerical dispersion amplitude of all course data packet queues of all types; obtaining the overall coherence amplitude between the latest course data packet queue of all types and all course data packet queues of all types according to the numerical decrement attribute between the latest course data packet queue and all course data packet queues of the corresponding type, as well as the numerical smooth amplitude of all course data packet queues of all types; obtaining the overall effect amplitude of the latest course data packet queue of all types on all course data packet queues of all types according to the numerical definition difference and the overall coherence amplitude between the latest course data packet queue of all types and all course data packet queues of all types.

[0009] Furthermore, in Step 2, the method for obtaining the numerical dispersion amplitude specifically includes: In each category of past course data packet queues, the difference between the value of each numerical point and the average of all numerical points in each category of past course data packet queues is summed up to obtain the numerical dispersion amplitude of each category of past course data packet queues at each storage time point, in each category of past course data packet queues.

[0010] Furthermore, in Step 2, the calculation equation of the numerical dispersion amplitude is: In the equation, Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The first storage point The number of values ​​of the previous course data packet queues of each type; Representative The first storage point The first in the queue of past course data packets of the category The value of the numerical point; Representative The average of the values ​​in the data packet queues of past courses of the same category.

[0011] Furthermore, in Step 2, the method for obtaining the coherent importance specifically includes: The difference in the numerical dispersion amplitudes of any pair of past course data packets is inversely adjusted and normalized to obtain the relative importance of any pair of past course data packet queues at each storage time point.

[0012] Furthermore, in Step 2, the coherence importance calculation equation is: In the equation, Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of various categories; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Represents the use of Z-score method to Perform standardized disposal.

[0013] Furthermore, in Step 2, the method for obtaining the value fluctuation status distinction quantity specifically includes: The regression line of the past course data packet of each type at each storage time point is constructed by the least square method; the regression line of any pair of types of past course data packets at each storage time point is fitted by the LCSS method to obtain the whole value fitting cluster of any pair of types of past course data packet queues at each storage time point; the derivative difference between a pair of value points in the whole value fitting cluster of each storage time point is summed up to obtain the value fluctuation state difference of any pair of types of past course data packet queues at each storage time point.

[0014] Furthermore, in Step 2, the method for obtaining the numerical coherence degree specifically includes: Under any pair of categories, the product of the correlation importance and numerical fluctuation status difference between the previous course data packet queues at each storage time point is taken as the multiplication quantity one, and the total quantity of the multiplication quantity one of all storage time points is inversely adjusted and standardized to obtain the numerical correlation between all course data packet queues of any pair of categories.

[0015] Furthermore, in Step 2, the numerical coherence calculation equation is: In the equation, Representative All course data packets of the category are queued and The numerical correlation between all course data package queues of a category; Represents the number of storage points in the pre-set past period; Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of various categories; Representative The first storage point The previous course data packet queues and the The number of numerical adaptation clusters between the queues of past course data packets of each type; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; is the Euler number.

[0016] Furthermore, in Step 3, the method for obtaining the numerical smooth amplitude specifically includes: Randomly select a class as the target class; use the LCSS method to obtain the entire adaptation course data packet consisting of all adaptation clusters on the entire course data packet queue between each other class and the target class, and then execute the following equation: In the equation, Representative purpose type; The numerical amplitude of the whole course data packet queue representing the purpose category; The number of values ​​in the queue of all course data packets representing the target category; Represents the number of other categories; Representative purpose type and The numerical correlation between all course data sets of another type; Representative Another type of all course data packet queue facing the target type of all course data packet queue The smooth coherence amplitude of the numerical points; The first in the queue of all course data packets representing the target category The value of the numerical point; The average of all numerical points in all course data packet queues representing the target category; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The number of adapted course data packets in the adapted cluster where the numerical point is located; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The first value of the adaptation cluster where the value point is located The value of the adapted course data package; Representative The mean of all numerical points of all course data sets of another type; Represents the use of Z-score method to Implement standardized disposal; is the Euler number.

[0017] Furthermore, in Step 3, the method for obtaining the overall coherent amplitude specifically includes: In each recent course data packet queue, the minimum amount of difference between each numerical point and the corresponding type of course data packet queue is summed up to obtain the numerical reduction amplitude between each recent course data packet queue and each type of course data packet queue; the amount obtained by multiplying the numerical flat amplitude and the numerical reduction amplitude of all course data packet queues of each type in a pre-set past time period is summed up to obtain the overall correlation amplitude between all types of recent course data packet queues and all types of course data packet queues.

[0018] Furthermore, in Step 3, the operational equation of the overall coherent amplitude is: In the equation, Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the number of types of all course data packet queues; Representative The numerical amplitude of the whole course data packet queue of each type; Representative The number of values ​​in the most recent course packet queue; Representative The most recent course packet queue The minimum amount of difference between individual data and the corresponding type of all course data packet queues.

[0019] Furthermore, in Step 3, the method for obtaining the numerical definition difference quantity specifically includes: The quotient obtained by dividing the number of values ​​in the latest course data package queue of all categories by the total number of values ​​in all course data package queues of all categories is used as the value defining difference between the latest course data package queue and the course data package queue of all categories.

[0020] Furthermore, in Step 3, the method for obtaining the overall action amplitude specifically includes: The quantity obtained by multiplying the numerical definition difference and the overall correlation amplitude is normalized using the Z-score method to obtain the overall effect amplitude of the nearest course packet queue on all course packet queues of all types.

[0021] Furthermore, in Step 3, the overall action amplitude calculation equation is: In the equation, Represents the total effect amplitude of the latest course data packet queues of all categories on the total course data packet queues of all categories; The number of values ​​representing the most recent course packet queues of all categories; The total number of values ​​representing all course data packet queues of all categories; Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the use of Z-score method to Perform standardized disposal.

[0022] Further, in Step 4, the method of grouping the recent course data packet queue according to the overall action amplitude specifically includes: All past course data packets within a preset past time period are grouped by the fuzzy C-means grouping method to obtain source groups; a critical value of one is set in advance, and all recent course data packet queues with action amplitudes lower than the critical value of one are cut into source groups by the DBSCAN method to obtain final groups; the recent course data packet queues with all action amplitudes higher than the critical value of one are directly grouped to obtain all the latest groups of the recent course data packet queue; all the latest groups and source groups are regarded as the final groups.

[0023] Furthermore, in Step 4, the RLE method can achieve good coding reduction effect for redundant values ​​by applying the RLE method to all values ​​in the final group for coding and storage.

[0024] A device for adjusting course information based on natural language processing, comprising: A forming module is used to obtain the latest course data packet queue of all types to be stored and the past course data packet queue of each type at each storage time point in the pre-set past period; the past course data packet queue of each type at all storage time points forms the whole course data packet queue of each type; A coherence module, which is used to obtain the numerical coherence of a pair of course data packet queues according to the numerical dispersion amplitude of a pair of past course data packet queues; An effect module, which is used to obtain the total effect amplitude of the nearest course data packet queue of all types on the total course data packet queue of all types according to the numerical correlation between the total course data packet queue of each type and the total course data packet queue of each other type; The storage module is used to obtain the latest course data packet queue and the final grouping of all course data packet queues of all types according to the total action amplitude; and encode and store all the latest course data packets according to the final grouping.

[0025] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: Obtain the past course data packet queues at each storage time point in the past period and the latest course data packet queue to be stored; because the correlation between different types of course data packet queues will have a significant effect on the grouping and cutting of all types of course data packet queues, and the past course data packet queues at different storage time points often show some changes with the change of the storage time point, so according to the numerical dispersion amplitude of a random pair of types of past course data packet queues, obtain the correlation importance of a random pair of types of past course data packet queues at each storage time point, reflecting the stability of the data of a random pair of types of past course data packet queues at each storage time point; adjust the numerical fluctuation state distinction of a random pair of past course data packet queues at each storage time point through the correlation importance, and obtain the numerical correlation of all course data packet queues of a random pair of types; because the numerical dispersion amplitude of all course data packet queues of a type It often has a significant effect on the data stability amplitude of all course data packet queues of other types, thereby obtaining the numerical smooth amplitude of all course data packet queues of each type; in addition, because the latest course data packet queue will have some effect on all course data packet queues of all types, thereby involving the existing grouping of all past course data packets, thus obtaining the overall effect amplitude of all types of the latest course data packet queue on all types of course data packet queues, and using the overall effect amplitude to obtain the final grouping of the latest course data packet queue and all types of course data packet queues; encoding all recent course data packets according to the final grouping, the invention involves the correlation between different types of past course data packets and the effect of the latest course data packet on the past course data packets, and obtains a grouping that accurately reflects the approximate numerical arrangement, thereby obtaining an accurate course data packet storage function. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of the method for adjusting course information based on natural language processing in the present invention; Figure 2 It is a partial structural diagram of the device for adjusting course information based on natural language processing described in the present invention. DETAILED DESCRIPTION

[0027] To improve the efficiency of course data packet encoding, several types of course data packets formed by the grouping method are often grouped and cut, and the groups are encoded and reduced for storage. At present, when adding a newly formed recent course data packet (that is, a newly formed course data packet) to a group formed by existing past course data packets (that is, past stored course data packets), the effect of the recent course data packet on the past course data packets is not taken into account, which often damages the existing grouping and destroys the course data packet, making the encoded data incomplete, thereby failing to obtain accurate course data packet storage effect. The present invention relates to the correlation between different types of past course data packets and the effect of the recent course data packet on the past course data packets, and obtains a grouping that accurately reflects the arrangement of approximate values, thereby obtaining accurate course data packet storage effect.

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely express the technical solution of the present invention. The embodiments expressed in this application are only some embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0029] like Figure 1 As shown, the method for adjusting course information based on natural language processing of the present invention includes: Based on natural language processing of course information, until the teaching completion degree corresponding to the course is formed, wherein the method based on natural language processing of course information is to form a course data packet and store it in a database, obtain a recognition model through a natural language model and other models (natural language models and other models include training vector machines, natural language models and image models), perform recognition on the course data packet in the database to obtain a corresponding completion state vector, and the course data packet contains manually organized courses and corresponding teaching images, teaching audio, and classroom personnel coordinates; obtain a recognition model through a natural language model and other models (natural language models and other models include training vector machines, natural language models and image models), perform recognition on the course data packet in the database to obtain a corresponding completion state vector, until the teaching completion degree corresponding to the course is formed, which can be achieved by the corresponding method in the patent publication number "CN118279105A". The types of course data packets are several types such as manually organized courses and corresponding teaching images, teaching audio, and classroom personnel coordinates.

[0030] During the process of forming the course data package and storing it in the database, it also includes: Step 1, obtain the latest course data packet queue of all types to be stored and the past course data packet queue of all types at each storage time point in the pre-set past time period; the past course data packet queue of all types at all storage time points forms the whole course data packet queue of all types; the latest course data packet queue is the queue formed by arranging the latest course data packets in the order of their formation, the latest course data packet is the newly formed course data packet, and the past time period is the previous time period. The past course data packet queue is the queue formed by arranging the past course data packets in the order of their storage, and the past course data packets are the course data packets stored in the pre-set past time period.

[0031] The course information adjustment method based on natural language processing of the present application performs storage on the formed course data packet, and initially obtains the formed course data packet. Because the course data packet contains several types of manually organized courses and corresponding teaching images, teaching audios, and classroom personnel coordinates, all types of course data packets are obtained. Because under specific circumstances, a course data packet storage will be executed once a defined number of past course data packets are formed, so a certain number of past course data packets formed each time are formed into a past course data packet queue at each storage time point. Because the newly formed course data packet needs to be stored later, in the present application, all types of past course data packet queues at each storage time point and the latest course data packet queue to be stored are obtained, and all types of past course data packet queues at all storage time points in the pre-set past time period form all types of course data packet queues.

[0032] In a preferred but non-restrictive implementation of the present invention, in Step 1, because the number of types and the number of values ​​of the generated past course data packets and the latest course data packets are very large, directly performing subsequent analysis will often cause the different types of past course data packets to be unbalanced when performing subsequent grouping, thereby causing errors. Therefore, the text information in the obtained course data packets is transformed into attribute vectors through the Word2Vec model, and the obtained entire attribute vector is processed by the ridge regression method, and the information volume of several types of course data packets is concentrated and transformed into a length-normalized structure for subsequent processing.

[0033] The size of the past period can be set to one day.

[0034] Therefore, a queue of past course data packets of all types at each storage time point and a queue of the latest course data packets to be stored are obtained.

[0035] Step 2, according to the numerical dispersion amplitude of a random pair of types of past course data packet queues, obtain the correlation importance of a random pair of types of past course data packet queues at each storage time point; according to the numerical fluctuation state distinction and correlation importance of a random pair of types of past course data packet queues at each storage time point, obtain the numerical correlation of a random pair of types of course data packet queues; There are often some correlations between different types of past course data packages, just as the larger the number of teaching images, the larger the number of teaching audios; and the correlations between different types of past course data packages will have a corresponding effect on the grouping and cutting of all types of course data package queues, often resulting in an imbalance in grouping and cutting. Therefore, in this application, it is necessary to obtain the numerical correlation of any pair of types of all course data package queues at each storage time point.

[0036] The past course data packet queues at different storage points will often show some changes with the change of the storage point. Therefore, it is necessary to perform separate analysis on the past course data packet queues at each storage point. For the past course data packet queues at different storage points, the numerical dispersion amplitude of each type of past course data packet queue reflects its stability, and the higher the stability of the past course data packet queue, the closer the correlation between the calculation of this type of past course data packet queue and other types of past course data packet queues. Therefore, in this application, the correlation importance of any pair of types of past course data packet queues at each storage time point is obtained based on the numerical dispersion amplitude of any pair of types of past course data packet queues.

[0037] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the numerical dispersion amplitude specifically includes: In each type of past course data packet queue, the difference between the value of each numerical point and the average of all numerical points in each type of past course data packet queue is summed up to obtain the numerical dispersion amplitude of each type of past course data packet queue at each storage time point. In each type of past course data packet queue, the higher the difference between the value of each numerical point and the average of all numerical point values, the more dispersed each past course data packet is relative to the entire past course data packet queue. A numerical point is a corresponding course data packet (here is a past course data packet), and the value of the numerical point is the specific value of the binary value of the numerical point.

[0038] In a preferred but non-limiting embodiment of the present invention, in Step 2, the operation equation of the numerical dispersion amplitude is: In the equation, Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The first storage point The number of values ​​of the previous course data packet queues of each type; Representative The first storage point The first in the queue of past course data packets of the category The value of the numerical point; Representative The average of the values ​​in a category of past course data packets (that is, the average of the values ​​of all numerical points in the queue).

[0039] In the numerical dispersion amplitude calculation equation, The higher the difference between the values ​​of each value point and the average of all value points in the previous course data packet queue of the same type, the higher the difference between each value point and the average of all value points. The more dispersed the previous course data packet queue of each type is, the difference between each value point and the average of all value points is summed up. The higher the total value, the higher the difference. The higher the numerical dispersion of the past course data packet queue of each type, the higher the numerical dispersion of the past course data packet queue of each type.

[0040] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the coherence importance specifically includes: The difference in the numerical dispersion amplitudes of any pair of past course data packets is inversely adjusted and normalized to obtain the relative importance of any pair of past course data packet queues at each storage time point.

[0041] In a preferred but non-limiting embodiment of the present invention, in Step 2, the coherence importance calculation equation is: In the equation, Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of each category; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Represents the use of Z-score method to Perform standardized disposal.

[0042] In the coherent importance calculation equation, the lower the difference in the numerical dispersion amplitude between a pair of past course data packet queues, the better the Category of past course data packet queues and The previous course data packet queue of the category is in The more similar the arrangement of values ​​at each storage time point is, the The first of the storage time points Category of past course data packet queues and the first The higher the correlation between the data packages of the same type, the greater the correlation importance.

[0043] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the value fluctuation status distinction amount specifically includes: Use the least squares method to construct the regression line of the past course data package of each type at each storage time point (the horizontal axis is the storage time point, and the vertical axis is the value of the course data package); use the LCSS method to fit the regression lines of any pair of types of past course data packages at each storage time point (fitting means that each point on one regression line of the pair of regression lines is matched on the other regression line), and obtain all the value matching clusters of any pair of types of past course data package queues at each storage time point (a value matching cluster is two points on a pair of regression lines that match each other); sum up the derivative difference between a pair of value points in all the value matching clusters at each storage time point (that is, the absolute value of the difference obtained by subtracting the derivative values ​​of a pair of value points on their regression lines) to obtain the value fluctuation difference of any pair of types of past course data package queues at each storage time point. The higher the value fluctuation difference, the lower the correlation between any pair of types of past course data package queues.

[0044] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the numerical coherence degree specifically includes: Under any pair of categories, the product of the correlation importance and numerical fluctuation status difference between the previous course data packet queues at each storage time point is taken as the multiplication quantity one, and the total quantity of the multiplication quantity one of all storage time points is inversely adjusted and standardized to obtain the numerical correlation between all course data packet queues of any pair of categories.

[0045] In a preferred but non-limiting embodiment of the present invention, in Step 2, the numerical coherence calculation equation is: In the equation, Representative All course data packets of the category are queued and The numerical correlation between all course data package queues of a category; Represents the number of storage points in the pre-set past period; Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of each category; Representative The first storage point The previous course data packet queues and the The number of numerical adaptation clusters between the queues of past course data packets of each type; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; Representative The first storage point The previous course data packet queues and the The difference in the numerical fluctuations between the data packets of the past courses of each type; is the Euler number.

[0046] In the numerical coherence calculation equation, The previous course data packet queues and the The lower the difference in derivatives between a pair of numerical points in each numerical adaptation cluster between the data packets of the previous courses of the same type, the closer the numerical fluctuations between the numerical adaptation clusters are. The first storage point The previous course data packet queues and the The sum of the value fluctuation differences of each value adaptation cluster between the past course data packet queues of the same type is obtained. The first storage point The previous course data packet queues and the The difference in the value fluctuation between the data packets of the previous courses of the same type, and the higher the value fluctuation difference at this time; if at this time The first storage point The previous course data packet queues and the The higher the correlation importance between the past course data packet queues of a type, the higher the correlation between a pair of past course data packet queues of a type, that is, The first storage point The previous course data packet queues and the The higher the effect of the difference in numerical fluctuations between different types of past course data packets on the correlation between a pair of types of course data packets, the higher the numerical correlation between a pair of types of past course data packets obtained by multiplying the correlation importance of any pair of types of past course data packets within the storage time point by the difference in numerical fluctuations will be.

[0047] Step 3, according to the numerical correlation between all course data packet queues of each type and all course data packet queues of each other type, as well as the numerical dispersion amplitude of all course data packet queues of each type, obtain the numerical mean amplitude of all course data packet queues of each type; according to the numerical reduction attribute between the latest course data packet queue and all course data packet queues of the corresponding type, as well as the numerical mean amplitude of all course data packet queues of each type, obtain the total correlation amplitude between the latest course data packet queue of all types and all course data packet queues of all types; according to the numerical definition difference and the total correlation amplitude between the latest course data packet queue of all types and all course data packet queues of all types, obtain the total effect amplitude of the latest course data packet queue of all types on all course data packet queues of all types; Because different types of past course data packets often have some correlation, the numerical dispersion amplitude of one type of past course data packets will often have a corresponding effect on the stable amplitude of other types of past course data packets. In order to prevent each type of past course data packets from being affected by other types of past course data packets, in this application, the numerical stability amplitude of each type of course data packet queue is obtained based on the numerical correlation between each type of course data packet queue and all other types of course data packet queues, as well as the numerical dispersion amplitude of each type of course data packet queue.

[0048] In a preferred but non-limiting embodiment of the present invention, in Step 3, the method for obtaining the numerical smooth amplitude specifically includes: Randomly select a class as the target class; use the LCSS method to obtain all the adaptation clusters on all the course data packet queues between each other class and the target class (the adaptation cluster is the course data packet on one of a pair of course data packet queues that uses the LCSS method to find the adapted course data packet on the other course data packet queue) to form all the adapted course data packets, and then execute the following equation: In the equation, Representative purpose type; The numerical amplitude of the whole course data packet queue representing the purpose category; The number of values ​​in the queue of all course data packets representing the target category; Represents the number of other categories; Representative purpose type and The numerical correlation between all course data sets of another type; Representative Another type of all course data packet queue facing the target type of all course data packet queue The smooth coherence amplitude of the numerical points; The first in the queue of all course data packets representing the target category The value of the numerical point; The average of all numerical points in all course data packet queues representing the target category; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The number of adapted course data packets in the adapted cluster where the numerical point is located; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The first value of the adaptation cluster where the value point is located The value of the adapted course data package; Representative The mean of all numerical points of all course data sets of another type; Represents the use of Z-score method to Implement standardized disposal; is the Euler number.

[0049] Facing the Another type of course packet queue facing the entire course packet queue of the target type For the smooth coherence amplitude of the numerical point, if the The value of the adapted course data package and the The lower the mean difference of the values ​​of all course data sets of the other types, the higher the The adapted course data package corresponds to The overall course data packet queue of another type is generally flatter. The average of the quotient obtained by dividing the dispersion amplitude of all adapted course data packets by 1 can be obtained to obtain the overall course data packet queue of the target type. The smooth coherence amplitude of the entire adaptation course data package at each numerical point.

[0050] In the numerical smooth amplitude calculation equation, for each numerical point of the target type course data packet queue, the total amount of the amount obtained by multiplying the smooth coherent amplitude of the adapted course data packet of another type and the coherence between a pair of types is calculated as the passive amplitude of each numerical point of the target type. The higher the coherence here and the higher the smooth coherent amplitude of the adapted course data packet of another type, it means that the numerical fluctuation of each numerical point of the target type will be more affected by the previous course data packet of another type, and the passive amplitude of each numerical point of the target type will be higher; the passive amplitude of each numerical point of the target type is summed up, the higher the total amount, the lower the numerical smooth amplitude of the entire course data packet queue of the target type, and the numerical smooth amplitude reflects the effect of other types on the target type.

[0051] When storing past course data packets, the similarly arranged values ​​are often grouped and cut, and then each group is encoded and stored. When storing the latest course data packet queue to be stored, the latest course data packet queue must be stored under the condition of the stored past course data packets, that is, each newly formed latest course data packet queue will not affect the cutting status of the past course data packet grouping, because the definition difference between the newly formed latest course data packet queue and all past course data packets will have a significant impact on the past course data packet grouping, so it is necessary to involve the impact of the latest course data packet queue to be stored on all past course data packets. Therefore, in this application, the overall impact amplitude of the latest course data packet queue on all course data packet queues of all types is obtained.

[0052] During the grouping of the latest course data packet queue, the difference between the latest course data packet queue and the corresponding type of all course data packet queue is involved. The higher the difference, the higher the impact of the latest course data packet queue on the all course data packet queue. And all course data packet queues of each type are often affected by all course data packet queues of other types. Therefore, when the overall correlation amplitude between the latest course data packet queue and the corresponding type of course data packet queue is involved, the numerical smooth amplitude of all course data packet queues of each type must be analyzed. Therefore, in this application, the overall correlation amplitude between the latest course data packet queue of all types and all course data packet queues of all types is obtained based on the numerical reduction attribute between the latest course data packet queue and the corresponding type of all course data packet queue, as well as the numerical smooth amplitude of all course data packet queues of each type.

[0053] In a preferred but non-limiting embodiment of the present invention, in Step 3, the method for obtaining the overall coherent amplitude specifically includes: In each recent course data packet queue, the minimum amount of difference between each numerical point and the corresponding type of course data packet queue is summed up to obtain the numerical reduction amplitude between each recent course data packet queue and each type of course data packet queue; the amount obtained by multiplying the numerical flat amplitude and the numerical reduction amplitude of all course data packet queues of each type in a pre-set past time period is summed up to obtain the overall correlation amplitude between all types of recent course data packet queues and all types of course data packet queues.

[0054] In a preferred but non-limiting embodiment of the present invention, in Step 3, the operation equation of the overall coherent amplitude is: In the equation, Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the number of types of all course data packet queues; Representative The numerical amplitude of the whole course data packet queue of each type; Representative The number of values ​​in the most recent course packet queue; Representative The most recent course packet queue The minimum amount of difference between individual data and the corresponding type of all course data packet queues.

[0055] In the overall coherence amplitude calculation equation, Through the The minimum value of the difference between each value of the most recent course data packet queue and all types of course data packet queues reflects the effect of the most recent course data packet on all past course data packets. Here, the higher the minimum value of the total difference between the most recent course data packet queue and all types of course data packet queues, the more special the most recent course data packet queue is for all types of course data packet queues, and the more analysis is required for all types of course data packet queues. At this time, if the overall constant amplitude of all types of course data packet queues themselves is higher, it means that the correlation amplitude of the most recent course data packet queue for all types of course data packet queues is higher. When the correlation amplitudes are added up, the overall correlation amplitude between the most recent course data packet queues of all types and all types of course data packet queues is higher.

[0056] Because the difference between the numerical definition of the latest course data packet queue and the numerical definition of all course data packet queues of all types also affects the subsequent grouping of the latest course data packet queue, the higher the numerical definition of the latest course data packet queue, the higher the effect of the latest course data packet queue data on all types of course data packet queues. Therefore, in this application, the overall effect amplitude of the latest course data packet queue on all course data packet queues of all types is obtained based on the numerical definition difference and overall correlation amplitude between the latest course data packet queue of all types and the all course data packet queues of all types.

[0057] In a preferred but non-limiting embodiment of the present invention, in Step 3, the method for obtaining the numerical definition difference quantity specifically includes: The quotient obtained by dividing the number of values ​​in the latest course data package queue of all types by the total number of values ​​in all course data package queues of all types is used as the value boundary difference between the latest course data package queue and all course data package queues of all types. The closer the ratio is to one, the closer the value boundary between the latest course data package queue data and all course data package queues of all types is, and the effect of the latest course data package queue data on all course data package queues of all types is higher; if the quotient is higher than one, then the value boundary of the latest course data package queue is higher than that of all course data package queues of all types, and the effect of the latest course data package queue data on all course data package queues of all types is higher.

[0058] In a preferred but non-limiting embodiment of the present invention, in Step 3, the method for obtaining the overall action amplitude specifically includes: The quantity obtained by multiplying the numerical definition difference and the overall correlation amplitude is normalized using the Z-score method to obtain the overall effect amplitude of the nearest course packet queue on all course packet queues of all types.

[0059] In a preferred but non-limiting embodiment of the present invention, in Step 3, the overall action amplitude calculation equation is: In the equation, Represents the total effect amplitude of the latest course data packet queues of all categories on the total course data packet queues of all categories; The number of values ​​representing the most recent course packet queues of all categories; The total number of values ​​representing all course data packet queues of all categories; Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the use of Z-score method to Perform standardized disposal.

[0060] In the overall effect amplitude calculation equation, the higher the effect amplitude of the latest course data packet queue of all types on the overall course data packet queue of all types, and the higher the numerical definition difference between the overall latest course data packet queue and the overall course data packet queue of all types, it means that when grouping all the latest course data packet queues, the overall effect amplitude of the overall latest course data packet queue on the overall course data packet queue of all types is higher.

[0061] Therefore, the overall effect amplitude of the latest course data packet queue of all types on the course data packet queue of all types is obtained.

[0062] Step 4, according to the total action amplitude, obtain the last grouping of the latest course data packet queue and the last grouping of the entire course data packet queue of all types; according to the last grouping, encode and store all the latest course data packets. The method of performing recognition on the course data packets in the database to obtain the corresponding completion state vector includes taking out the encoded course data packet in the database and performing decoding and restoration first, and then performing recognition on the decoded and restored course data packet to obtain the corresponding completion state vector.

[0063] When grouping the latest course data packet queue, the latest course data packet queue is often cut into the existing grouping of all previous course data packets, and the latest course data packet queue is often grouped again. Here, the specific grouping status depends on the overall effect amplitude obtained in Step 4. Therefore, in this application, the final grouping of the latest course data packet queue and all types of course data packet queues is obtained according to the overall effect amplitude.

[0064] In a preferred but non-limiting embodiment of the present invention, in Step 4, the method of grouping the recent course data packet queue according to the overall action amplitude specifically includes: The fuzzy C-means grouping method is used to group all past course data packets within a preset past period to obtain source groups; a critical value of one is set in advance, and the DBSCAN method is used to cut all recent course data packet queues with action amplitudes lower than the critical value of one into source groups to obtain the final group; the recent course data packet queues with all action amplitudes higher than the critical value of one are directly grouped (that is, the recent course data packet queue is independently formed into a group), and all the latest groups of the recent course data packet queue are obtained; all the latest groups and source groups are regarded as the final group. The value of the critical value of one can be 60%.

[0065] In a preferred but non-restrictive implementation of the present invention, in Step 4, the values ​​cut into the same group will have the same type of value arrangement, and many similar redundant values ​​can be encoded and stored by means of encoding reduction. The RLE method can have a good encoding reduction effect for redundant values, and the encoding and storage is performed by applying the RLE method to all values ​​in the last group.

[0066] like Figure 2 As shown, the device for adjusting course information based on natural language processing according to the present invention comprises: A forming module is used to obtain the latest course data packet queue of all types to be stored and the past course data packet queue of each type at each storage time point in the pre-set past period; the past course data packet queue of each type at all storage time points forms the whole course data packet queue of each type; A coherence module, which is used to obtain the numerical coherence of a pair of course data packet queues according to the numerical dispersion amplitude of a pair of past course data packet queues; An effect module, which is used to obtain the total effect amplitude of the nearest course data packet queue of all types on the total course data packet queue of all types according to the numerical correlation between the total course data packet queue of each type and the total course data packet queue of each other type; The storage module is used to obtain the latest course data packet queue and the final grouping of all course data packet queues of all types according to the total action amplitude; and encode and store all the latest course data packets according to the final grouping.

[0067] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: Obtain the past course data packet queues at each storage time point in the past period and the latest course data packet queue to be stored; because the correlation between different types of course data packet queues will have a significant effect on the grouping and cutting of all types of course data packet queues, and the past course data packet queues at different storage time points often show some changes with the change of the storage time point, so according to the numerical dispersion amplitude of a random pair of types of past course data packet queues, obtain the correlation importance of a random pair of types of past course data packet queues at each storage time point, reflecting the stability of the data of a random pair of types of past course data packet queues at each storage time point; adjust the numerical fluctuation state distinction of a random pair of past course data packet queues at each storage time point through the correlation importance, and obtain the numerical correlation of all course data packet queues of a random pair of types; because the numerical dispersion amplitude of all course data packet queues of a type It often has a significant effect on the data stability amplitude of all course data packet queues of other types, thereby obtaining the numerical smooth amplitude of all course data packet queues of each type; in addition, because the latest course data packet queue will have some effect on all course data packet queues of all types, thereby involving the existing grouping of all past course data packets, thus obtaining the overall effect amplitude of all types of the latest course data packet queue on all types of course data packet queues, and using the overall effect amplitude to obtain the final grouping of the latest course data packet queue and all types of course data packet queues; encoding all recent course data packets according to the final grouping, the invention involves the correlation between different types of past course data packets and the effect of the latest course data packet on the past course data packets, and obtains a grouping that accurately reflects the approximate numerical arrangement, thereby obtaining an accurate course data packet storage function.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. A method for adjusting course information based on natural language processing, characterized in that: include: Processing the course information based on natural language until the teaching completion degree corresponding to the course is formed, wherein the method of processing the course information based on natural language includes forming a course data package and storing it in a database; During the process of forming the course data package and storing it in the database, it also includes: Step 1, obtain the latest course data packet queue of all types to be stored and the past course data packet queue of all types at each storage time point in the pre-set past period; the past course data packet queue of all types at all storage time points forms the whole course data packet queue of all types; Step 2, according to the numerical dispersion amplitude of the previous course data packet queues of any pair of types, until the numerical coherence of the course data packet queues of any pair of types is obtained; Step 3, according to the numerical correlation between the whole course data packet queue of each type and the whole course data packet queue of each other type, until the whole effect amplitude of the nearest course data packet queue of all types on the whole course data packet queue of all types is obtained; Step 4, obtain the final grouping of the latest course data packet queue and all course data packet queues of all types according to the total effect amplitude; encode and store all the latest course data packets according to the final grouping.

2. The method for adjusting course information based on natural language processing according to claim 1, characterized in that: In Step 1, the text information in the obtained course data package is transformed into an attribute vector through the Word2Vec model, and the entire attribute vector obtained is processed by the ridge regression method; In Step 2, the method of obtaining the numerical coherence of a random pair of types of course data packet queues according to the numerical dispersion amplitude of a random pair of types of past course data packet queues includes: obtaining the coherence importance of a random pair of types of past course data packet queues at each storage time point according to the numerical dispersion amplitude of a random pair of types of past course data packet queues; obtaining the numerical coherence of a random pair of types of course data packet queues according to the numerical fluctuation state distinction and coherence importance of a random pair of types of past course data packet queues at each storage time point; In Step 3, the method of obtaining the overall effect amplitude of the latest course data packet queue of all types on all course data packet queues of all types according to the numerical coherence between all course data packet queues of all types and all course data packet queues of all other types, includes: obtaining the numerical smooth amplitude of all course data packet queues of all types according to the numerical coherence between all course data packet queues of all types and all course data packet queues of all other types, as well as the numerical dispersion amplitude of all course data packet queues of all types; obtaining the overall coherence amplitude between the latest course data packet queue of all types and all course data packet queues of all types according to the numerical decrement attribute between the latest course data packet queue and all course data packet queues of the corresponding type, as well as the numerical smooth amplitude of all course data packet queues of all types; obtaining the overall effect amplitude of the latest course data packet queue of all types on all course data packet queues of all types according to the numerical definition difference and the overall coherence amplitude between the latest course data packet queue of all types and all course data packet queues of all types.

3. The method for adjusting course information based on natural language processing according to claim 2, characterized in that: In Step 2, the method for obtaining the numerical dispersion amplitude includes: In each type of past course data packet queue, the difference between the value of each numerical point and the average of all numerical points in each type of past course data packet queue is summed up to obtain the numerical dispersion amplitude of each type of past course data packet queue at each storage time point, in each type of past course data packet queue; In Step 2, the calculation equation of the numerical dispersion amplitude is: In the equation, Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The first storage point The number of values ​​of the previous course data packet queues of each type; Representative The first storage point The first in the queue of past course data packets of the category The value of the numerical point; Representative The average of the values ​​in the data packet queues of the past courses of each category; In Step 2, the method for obtaining the coherent importance includes: The difference in the numerical dispersion amplitude of any pair of past course data packets is inversely proportionally adjusted and normalized to obtain the relevant importance of any pair of past course data packet queues at each storage time point; In Step 2, the coherence importance calculation equation is: In the equation, Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of various categories; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Representative The numerical dispersion amplitude of the data packet queues of the past courses of each category; Represents the use of Z-score method to Perform standardized disposal.

4. The method for adjusting course information based on natural language processing according to claim 3, characterized in that: In Step 2, the method for obtaining the value fluctuation status distinction quantity specifically includes: The regression line of the past course data packet of each type at each storage time point is constructed by the least square method; the regression line of any pair of types of past course data packets at each storage time point is fitted by the LCSS method to obtain the whole value fitting cluster of any pair of types of past course data packet queues at each storage time point; the derivative difference between a pair of value points in the whole value fitting cluster of each storage time point is summed up to obtain the value fluctuation state difference of any pair of types of past course data packet queues at each storage time point.

5. The method for adjusting course information based on natural language processing according to claim 4, characterized in that: In Step 2, the method for obtaining the numerical coherence degree specifically includes: Under any pair of categories, the value obtained by multiplying the correlation importance and the difference in numerical fluctuation between the past course data packet queues at each storage time point is taken as the multiplication value one, and the total amount of the multiplication value one of all storage time points is inversely proportionally allocated and normalized to obtain the numerical correlation between all course data packet queues of any pair of categories; In Step 2, the numerical coherence calculation equation is: In the equation, Representative All course data packets of the category are queued and The numerical correlation between all course data package queues of a category; Represents the number of storage points in the pre-set past period; Representative The first storage point The previous course data packet queues and the The coherence importance between the data packets of past courses of various categories; Representative The first storage point The previous course data packet queues and the The number of numerical adaptation clusters between the queues of past course data packets of each type; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; Representative The first storage point The previous course data packet queue of the category is in The derivative value of the numerical point in the numerical adaptation cluster on its regression line; is the Euler number.

6. The method for adjusting course information based on natural language processing according to claim 5, characterized in that: In Step 3, the method for obtaining the numerical smooth amplitude includes: Randomly select a class as the target class; use the LCSS method to obtain the entire adaptation course data packet consisting of all adaptation clusters on the entire course data packet queue between each other class and the target class, and then execute the following equation: In the equation, Representative purpose type; The numerical amplitude of the whole course data packet queue representing the purpose category; The number of values ​​in the queue of all course data packets representing the target category; Represents the number of other categories; Representative purpose type and The numerical correlation between all course data sets of another type; Representative Another type of all course data packet queue facing the target type of all course data packet queue The smooth coherence amplitude of the numerical points; The first in the queue of all course data packets representing the target category The value of the numerical point; The average of all numerical points in all course data packet queues representing the target category; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The number of adapted course data packets in the adapted cluster where the numerical point is located; Representative In the whole course package queue of another type, and in the whole course package queue of the target type The first value of the adaptation cluster where the value point is located The value of the adapted course data package; Representative The mean of all numerical points of all course data sets of another type; Represents the use of Z-score method to Implement standardized disposal; is the Euler number.

7. The method for adjusting course information based on natural language processing according to claim 6, characterized in that: In Step 3, the method for obtaining the overall coherent amplitude includes: In each recent course data packet queue, the minimum amount of the difference between each numerical point and the corresponding type of course data packet queue is summed up to obtain the numerical reduction amplitude between each recent course data packet queue and each type of course data packet queue; the amount obtained by multiplying the numerical flat amplitude and the numerical reduction amplitude of all course data packet queues of all types in the pre-set past period is summed up to obtain the overall correlation amplitude between all types of recent course data packet queues and all types of course data packet queues; In Step 3, the operational equation for the overall coherent amplitude is: In the equation, Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the number of types of all course data packet queues; Representative The numerical amplitude of the whole course data packet queue of each type; Representative The number of values ​​in the most recent course packet queue; Representative The most recent course packet queue The minimum amount of difference between individual data and the corresponding type of all course data packet queues.

8. The method for adjusting course information based on natural language processing according to claim 7, characterized in that: In Step 3, the method for obtaining the numerical definition difference quantity includes: The quotient obtained by dividing the number of values ​​in the latest course data package queue of all types by the total number of values ​​in the entire course data package queue of all types is used as the value definition difference between the latest course data package queue and the entire course data package queue; In Step 3, the method for obtaining the overall action amplitude includes: The quantity obtained by multiplying the numerical definition difference and the overall correlation amplitude is normalized using the Z-score method to obtain the overall effect amplitude of the nearest course data packet queue on all course data packet queues of all types; In Step 3, the equation for the overall action amplitude is: In the equation, Represents the total effect magnitude of the latest course data packet queues of all categories on all course data packet queues of all categories; The number of values ​​representing the most recent course packet queues of all categories; The total number of values ​​representing all course data packet queues of all categories; Represents the total correlation amplitude between the most recent course data packet queues of all categories and the total course data packet queues of all categories; Represents the use of Z-score method to Perform standardized disposal.

9. The method for adjusting course information based on natural language processing according to claim 8, characterized in that: In Step 4, the method of grouping the recent course data packet queue according to the overall action amplitude includes: The fuzzy C-means grouping method is used to group all past course data packets within a preset past period to obtain source groups; a critical value of one is set in advance, and the DBSCAN method is used to cut all recent course data packet queues with action amplitudes lower than the critical value of one into source groups to obtain final groups; the recent course data packet queues with all action amplitudes higher than the critical value of one are directly grouped to obtain all the latest groups of the recent course data packet queues; all the latest groups and source groups are regarded as final groups; In Step 4, the RLE method can achieve good coding reduction effect for redundant values, and the coding and storage are performed by applying the RLE method to all values ​​in the final group.

10. A device for adjusting course information based on natural language processing, characterized in that: include: A forming module is used to obtain the latest course data packet queue of all types to be stored and the past course data packet queue of each type at each storage time point in the pre-set past period; the past course data packet queue of each type at all storage time points forms the whole course data packet queue of each type; A coherence module, which is used to obtain the numerical coherence of a pair of course data packet queues according to the numerical dispersion amplitude of a pair of past course data packet queues; An effect module, which is used to obtain the total effect amplitude of the nearest course data packet queue of all types on the total course data packet queue of all types according to the numerical correlation between the total course data packet queue of each type and the total course data packet queue of each other type; The storage module is used to obtain the latest course data packet queue and the final grouping of all course data packet queues of all types according to the total action amplitude; and encode and store all the latest course data packets according to the final grouping.

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