Digital interactive learning model system and operation method

Abstract

The invention relates to the technical field of digitization, in particular to a digitization interactive learning model system and an operation method.According to the digitization interactive learning model system, a digitization interactive learning model is tested, and a test question statement is input into the digitization interactive learning model; matching the output reply statement with a test reply statement corresponding to a pre-stored test question statement, respectively extracting features in the reply statement and the test reply statement, and analyzing the operation condition of the model based on the number of the same features, the polysemy condition of the test question statement is analyzed according to the number of the associated vocabularies of each feature in the test question statement, the influence degree of the test question statement on model operation is analyzed according to the polysemy condition of the test question statement, and the larger the number of the associated vocabularies is, the more paraphrases of the question statement are, and the larger the influence on model operation is; the operation condition of the model is analyzed according to the number of the associated vocabularies, and when the number of the associated vocabularies is large, the number of subsequent reply statements is increased to improve the interactive learning efficiency of the model.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to a digital interactive learning model system and an operating method. Background Art

[0002] The digital interactive learning model system is a system that combines modern information technology and educational theory. It improves learning efficiency and quality by providing highly interactive and personalized learning experience. The existing technology obtains the data and model of the current simulation system, and uses three-dimensional simulation technology to present the results of real-time interaction in the form of animation, so that operators can obtain a complete operational interactive experience. However, it does not consider the impact of polysemous keywords in the interactive process on the interactive learning process.

[0003] Chinese Patent Publication No.: CN114973835A discloses a digital deep immersive interactive learning model training system and implementation method, which has an equipment training simulation workbench, on which a virtual reality interactive result display module, an operation simulation module and an intelligent trainer module are arranged; the data interactive link framework is used to obtain the data and model of the current simulation system, and provide data interfaces and basic data interactive services for all modules of the equipment training simulation workbench. The obtained data information presents the results of real-time interaction in the form of animation through three-dimensional simulation technology. The system effectively enables operators to obtain a complete interactive operation experience, quickly learn complete equipment operation requirements, and shorten the lead time for machine operation.

[0004] However, the prior art still has the following problems:

[0005] Polysemous keywords will have an impact on interactive learning and affect the efficiency of interactive learning. Summary of the invention

[0006] To this end, the present invention provides a digital interactive learning model system and an operating method to overcome the problem in the prior art that polysemous keywords may affect interactive learning and affect the efficiency of interactive learning.

[0007] To achieve the above-mentioned purpose, the present invention provides a digital interactive learning model system. It includes:

[0008] A model building module, which is used to build a digital interactive learning model based on the response data;

[0009] A test question module, which is connected to the model building module, stores test question sentences and test answer sentences corresponding to the test question sentences, and is used to output the test question sentences;

[0010] A feature extraction module, which is connected to the test question module and is used to extract features in the test question sentence;

[0011] An interactive module, which is connected to the model building module and the feature extraction module, and is used to input the features of the single test question sentence extracted by the feature extraction module into the digital interactive learning model, and output a reply sentence of the digital interactive learning model;

[0012] A test verification module, which is connected to the test question module, the feature extraction module and the interactive module, and is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply sentence and the test reply sentence, to conduct a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module, and to determine the interference intensity type for the operation of the digital interactive learning model based on the number of associated features of the features in the test question sentence;

[0013] A self-adjusting module, which is connected to the test verification module, is used to determine a processing method for the question test based on the interference intensity type determined by the test verification module, including:

[0014] The feature extraction method is judged to be unqualified.

[0015] Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

[0016] The present invention provides an operation method of a digital interactive learning model, comprising:

[0017] Build a digital learning model;

[0018] Inputting a test reply statement into the digital learning model to obtain a reply statement, and analyzing whether the operation of the digital interactive learning model is qualified according to the matching degree between the test reply statement and the characteristics of the reply statement;

[0019] Conducting a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features of the test question sentences;

[0020] Determine the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, and determine that the feature extraction method is unqualified according to the interference intensity type,

[0021] Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

[0022] Furthermore, the test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply statement and the test reply statement, wherein:

[0023] The matching degree is the ratio of the number of features that are the same in the response sentence and the test response sentence based on the test verification module to the total number of features in the test response sentence.

[0024] Furthermore, the test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply statement and the test reply statement, including:

[0025] The test verification module determines whether the operation of the digital interactive learning model is qualified;

[0026] The test verification module determines whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module, or performs a secondary analysis,

[0027] The test verification module determines that the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence.

[0028] Furthermore, the test verification module performs a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity characterization parameters, wherein:

[0029] The feature quantity characterization parameter is the ratio of the number of features extracted by the feature extraction module to the total number of features of the test question sentence.

[0030] Furthermore, the test verification module performs a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity representation parameters, including:

[0031] The test verification module determines whether the operation of the digital interactive learning model is qualified, or determines whether the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence.

[0032] Furthermore, the test verification module determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, including: a strong interference type and a weak interference type.

[0033] Further, the self-adjusting module determines a processing method for the question test based on the interference intensity type determined by the test verification module, including:

[0034] The self-adjusting module determines that the method for extracting the features of the test question sentence is unqualified, or determines that the features of the test question sentence are ambiguous.

[0035] Furthermore, the self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, including:

[0036] Identify features in the test response sentences,

[0037] Identify features in the response sentence,

[0038] Features that are different between the test reply sentence and the reply sentence are determined as non-matching keywords.

[0039] Further, the self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, wherein:

[0040] The increase in the number of response sentences following the test question sentence is positively correlated with the number of non-matching keywords.

[0041] Compared with the prior art, the beneficial effect of the present invention lies in that, in the present invention, a digital interactive learning model is tested, a test question sentence is input into the digital interactive learning model, an output reply sentence is matched with a test reply sentence corresponding to a pre-stored test question sentence, features in the reply sentence and the test reply sentence are respectively extracted, and the operation of the model is analyzed based on the number of identical features. Taking into account the polysemy of some words, the polysemy of the test question sentence is analyzed according to the number of associated words of each feature in the test question sentence, so as to analyze the degree of influence of the test question sentence on the operation of the model according to the polysemy of the test question sentence. The more the number of associated words, the more interpretations of the question sentence, and the greater the influence on the operation of the model. The operation of the model is analyzed according to the number of associated words. When there are many associated words, the number of subsequent reply sentences is increased to improve the interactive learning efficiency of the model.

[0042] Furthermore, in the present invention, the number of corresponding reply sentences is increased according to the number of keywords that do not match the test reply sentence and the reply sentence. The more unmatched keywords there are, the greater the deviation between the reply sentence recognized by the model and the test reply sentence. The number of subsequent reply sentences is increased according to the number of unmatched keywords, and relevant sentences are output to improve the accuracy of the reply, thereby further improving the interactive learning efficiency of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a structural block diagram of the digital interactive learning model system of the present invention;

[0044] Figure 2 A flowchart of the operation method of the digital interactive learning model;

[0045] Figure 3A flow chart for analyzing whether the operation of the digital interactive learning model is qualified;

[0046] Figure 4 A flow chart for determining whether the operation of the digital interactive learning model is qualified for secondary analysis. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical data of the system of the present invention in the six months before this determination and the corresponding historical determination results. It can be understood by those skilled in the art that the determination method of the system of the present invention for a single parameter mentioned above can be to select the value with the highest proportion as the preset standard parameter according to the data distribution, use weighted summation to use the obtained value as the preset standard parameter, substitute each historical data into a specific formula and use the value obtained by the formula as the preset standard parameter or other selection methods, as long as the system of the present invention can clearly define different specific situations in the single determination process through the obtained values.

[0049] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0050] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0051] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] See also Figure 1 As shown, it is a structural block diagram of the digital interactive learning model system of the present invention.

[0053] The digital interactive learning model system provided by the embodiment of the present invention includes:

[0054] A model building module, which is used to build a digital interactive learning model based on the response data;

[0055] A test question module, which is connected to the model building module, stores test question sentences and test answer sentences corresponding to the test question sentences, and is used to output the test question sentences;

[0056] A feature extraction module, which is connected to the test question module and is used to extract features in the test question sentence;

[0057] An interactive module, which is connected to the model building module and the feature extraction module, and is used to input the features of the single test question sentence extracted by the feature extraction module into the digital interactive learning model, and output a reply sentence of the digital interactive learning model;

[0058] A test verification module, which is connected to the test question module, the feature extraction module and the interactive module, and is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply sentence and the test reply sentence, to conduct a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module, and to determine the interference intensity type for the operation of the digital interactive learning model based on the number of associated features of the features in the test question sentence;

[0059] A self-adjusting module, which is connected to the test verification module, is used to determine a processing method for the question test based on the interference intensity type determined by the test verification module, including:

[0060] The feature extraction method is judged to be unqualified.

[0061] Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

[0062] Specifically, in this embodiment, the specific structures of the model building module, the test question module, the feature extraction module, the interactive module, the test verification module, and the self-adjustment module are not limited, and they can be composed of logical components, and the logical components include a field programmable processor, a computer, and a microprocessor in the computer.

[0063] See also Figure 2 As shown, it is a flow chart of the operating method of the digital interactive learning model.

[0064] The operating method of the digital interactive learning model provided by the embodiment of the present invention includes:

[0065] Build a digital learning model;

[0066] Inputting a test reply statement into the digital learning model to obtain a reply statement, and analyzing whether the operation of the digital interactive learning model is qualified according to the matching degree between the test reply statement and the characteristics of the reply statement;

[0067] Conducting a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features of the test question sentences;

[0068] Determine the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, and determine that the feature extraction method is unqualified according to the interference intensity type,

[0069] Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

[0070] Specifically, the test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply statement and the test reply statement, wherein:

[0071] The matching degree is the ratio of the number of features that are the same in the response sentence and the test response sentence based on the test verification module to the total number of features in the test response sentence.

[0072] See also Figure 3 As shown, it is a flow chart for analyzing whether the operation of the digital interactive learning model is qualified.

[0073] Specifically, the test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply statement and the test reply statement, including:

[0074] If the matching degree is greater than or equal to the first preset matching degree standard threshold, the test verification module determines that the operation of the digital interactive learning model is qualified;

[0075] If the matching degree is less than the first preset matching degree standard threshold and greater than or equal to the second preset matching degree standard threshold, the test verification module determines whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module and performs a secondary analysis;

[0076] If the matching degree is less than the second preset matching degree evaluation value, the test verification module determines that the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question statement.

[0077] Specifically, in this embodiment, the matching degree is the ratio of the same features between the reply sentence and the test reply sentence, as well as the similar features (such as synonyms) to the total number of features of the test reply sentence. The first preset matching degree standard threshold is selected between the interval [0.85, 0.95], and the second preset matching degree evaluation value is selected between the interval [0.65, 0.8].

[0078] Specifically, the test verification module performs a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity representation parameters, wherein:

[0079] The feature quantity characterization parameter is the ratio of the number of features extracted by the feature extraction module to the total number of features of the test question sentence.

[0080] See also Figure 4 As shown, it is a flow chart for determining whether the operation of the secondary analysis digital interactive learning model is qualified.

[0081] Specifically, the test verification module performs a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity representation parameters, including:

[0082] If the feature quantity characterization parameter is greater than or equal to the preset feature quantity characterization parameter standard threshold, the test verification module determines that the operation of the digital interactive learning model is qualified;

[0083] If the feature quantity characterization parameter is less than the preset feature quantity characterization parameter standard threshold, the test verification module determines that the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question statement.

[0084] Specifically, in this embodiment, the preset characteristic quantity characterization parameter standard threshold is selected in the interval [0.65-0.75].

[0085] Specifically, the test verification module determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, including:

[0086] If the associated feature number is greater than or equal to a preset associated feature number, the test verification module determines that the operation of the digital interactive model is a strong interference type;

[0087] If the number of associated features is less than the preset number of associated features, the test verification module determines that the operation of the digital interactive model is a weak interference type.

[0088] Specifically, in this embodiment, the number of associated features is the number of synonymous features of each feature in the test question sentence. The preset number of associated features is obtained in advance. Several test question sentences are obtained, the features in the test question sentences are extracted, the synonymous features of each feature are retrieved respectively, the number of synonymous features is counted, and the mean number of synonymous features of each test question sentence is solved. The preset number of associated features is 0.8 to 0.9 times the mean.

[0089] Specifically, the self-adjusting module determines a processing method for the question test based on the interference intensity type determined by the test verification module, including:

[0090] If the operation of the digital interactive model is of a strong interference type, the self-adjusting module determines that the method for extracting the features of the test question sentence is unqualified;

[0091] If the operation of the digital interactive model is of a strong interference type, the self-adjustment module determines that the features of the test question sentence are ambiguous.

[0092] Specifically, the self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, including:

[0093] Identify features in the test response sentences,

[0094] Identify features in the response sentence,

[0095] Features that are different between the test reply sentence and the reply sentence are determined as non-matching keywords.

[0096] Specifically, the self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, wherein:

[0097] The increase in the number of response sentences following the test question sentence is positively correlated with the number of non-matching keywords.

[0098] In this embodiment, optionally,

[0099] The number of non-matching keywords is compared with a first preset number standard threshold and a second preset number standard threshold,

[0100] If the number of non-matching keywords is less than or equal to the first preset number standard threshold, the number of first reply statements is increased, and the number of first reply statements is 0.2 times the number of initial reply statements;

[0101] If the number of non-matching keywords is greater than the first preset number standard threshold and less than or equal to the second preset number standard threshold, then increase the number of second reply statements, and the number of second reply statements is 0.25 times the number of initial reply statements;

[0102] If the number of non-matching keywords is greater than the second preset quantity standard threshold, the number of third reply statements is increased, and the number of third reply statements is 0.3 times the number of initial reply statements.

[0103] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital interactive learning model system, characterized in that: include: A model building module, which is used to build a digital interactive learning model based on the response data; A test question module, which is connected to the model building module, stores test question sentences and test answer sentences corresponding to the test question sentences, and is used to output the test question sentences; A feature extraction module, which is connected to the test question module and is used to extract features in the test question sentence; An interactive module, which is connected to the model building module and the feature extraction module, and is used to input the features of the single test question sentence extracted by the feature extraction module into the digital interactive learning model, and output a reply sentence of the digital interactive learning model; A test verification module, which is connected to the test question module, the feature extraction module and the interactive module, and is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply sentence and the test reply sentence, to conduct a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module, and to determine the interference intensity type for the operation of the digital interactive learning model based on the number of associated features of the features in the test question sentence; A self-adjusting module, which is connected to the test verification module, is used to determine a processing method for the question test based on the interference intensity type determined by the test verification module, including: The feature extraction method is judged to be unqualified. Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

2. The digital interactive learning model system according to claim 1, characterized in that: The test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply sentence and the test reply sentence, wherein: The matching degree is the ratio of the number of features that are the same in the response sentence and the test response sentence based on the test verification module to the total number of features in the test response sentence.

3. The digital interactive learning model system according to claim 1, characterized in that: The test verification module is used to analyze whether the operation of the digital interactive learning model is qualified based on the matching degree between the reply sentence and the test reply sentence, including: The test verification module determines whether the operation of the digital interactive learning model is qualified; The test verification module determines whether the operation of the digital interactive learning model is qualified based on the number of features extracted by the feature extraction module, or performs a secondary analysis, The test verification module determines that the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence.

4. The digital interactive learning model system according to claim 1, characterized in that: The test verification module performs a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity representation parameters, wherein: The feature quantity characterization parameter is the ratio of the number of features extracted by the feature extraction module to the total number of features of the test question sentence.

5. The digital interactive learning model system according to claim 1, characterized in that: The test verification module performs secondary analysis on whether the operation of the digital interactive learning model is qualified based on the characteristic quantity representation parameters, including: The test verification module determines whether the operation of the digital interactive learning model is qualified, or determines whether the operation of the digital interactive learning model is unqualified, and determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence.

6. The digital interactive learning model system according to claim 1, characterized in that: The test verification module determines the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, including: a strong interference type and a weak interference type.

7. The digital interactive learning model system according to claim 6, characterized in that: The self-adjusting module determines a processing method for the question test based on the interference intensity type determined by the test verification module, including: The self-adjusting module determines that the method for extracting the features of the test question sentence is unqualified, or determines that the features of the test question sentence are ambiguous.

8. The digital interactive learning model system according to claim 1, characterized in that: The self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, including: Identify features in the test response sentences, Identify features in the response sentence, Features that are different between the test reply sentence and the reply sentence are determined as non-matching keywords.

9. The digital interactive learning model system according to claim 1, characterized in that: The self-adjusting module increases the number of reply statements following the corresponding test question statement based on the non-matching keywords between the test reply statement and the reply statement, wherein: The increase in the number of response sentences following the test question sentence is positively correlated with the number of non-matching keywords.

10. A method for operating a digital interactive learning model of the system according to any one of claims 1 to 9, characterized in that: include: Build a digital learning model; Inputting a test reply statement into the digital learning model to obtain a reply statement, and analyzing whether the operation of the digital interactive learning model is qualified according to the matching degree between the test reply statement and the characteristics of the reply statement; Conducting a secondary analysis on whether the operation of the digital interactive learning model is qualified based on the number of features of the test question sentences; Determine the interference intensity type for the operation of the digital interactive learning model based on the associated feature number of the features in the test question sentence, and determine that the feature extraction method is unqualified according to the interference intensity type, Or, determine that the test question sentence is ambiguous, and increase the number of reply sentences following the corresponding test question sentence based on non-matching keywords between the test reply sentence and the reply sentence.

Images