Teaching information processing system and method based on electrical automation control

Abstract

The invention discloses a teaching information processing system and method based on electrical automation control, and relates to the technical field of intelligent teaching, and the method comprises the steps: collecting multi-modal teaching information, and carrying out the preprocessing; extracting and fusing features by using the preprocessed multi-modal teaching information to generate learning state features; constructing and dynamically adjusting a knowledge graph by using the learning state features to obtain a dynamic knowledge graph; generating a personalized learning path based on the dynamic knowledge graph, recommending learning resources, and recording the utilization rate of the learning resources; generating a comprehensive learning state of the student based on the learning state feature and the learning resource utilization rate; and generating teaching feedback according to the comprehensive learning state. According to the invention, by constructing the dynamic knowledge graph and combining the learning state characteristics and the learning resource utilization rate of the students, the knowledge point mastering condition of the students is dynamically evaluated, the personalized learning path is generated, and the adaptive learning resources are pushed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent teaching, and particularly to a teaching information processing system and method based on electrical automation control. Background Art

[0002] In the teaching process of the electrical automation control course, traditional teaching methods often rely on the experience of teachers and the self-feedback of students, lacking comprehensive and real-time monitoring of students' learning status. With the development of information technology, although some educational platforms have begun to introduce data analysis to assist teaching, there are still many deficiencies in the existing solutions.

[0003] Most existing teaching systems can only process single-modal data, such as text or operation records, and cannot comprehensively analyze multi-modal information. This limitation makes it difficult for the system to accurately capture the real learning status of students, thereby affecting the effectiveness of personalized teaching paths. For example, in a highly practical course like electrical automation control, relying solely on the completion of homework or exam scores cannot fully reflect the actual mastery and problems encountered by students. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a teaching information processing method based on electrical automation control to solve the problems of insufficient comprehensive analysis of multi-modal data and lack of real-time feedback.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a teaching information processing method based on electrical automation control, which includes collecting multi-modal teaching information and performing preprocessing;

[0008] Using the preprocessed multi-modal teaching information, extracting and fusing features to generate learning status features;

[0009] Using the learning status features to construct and dynamically adjust a knowledge graph to obtain a dynamic knowledge graph;

[0010] Generating a personalized learning path based on the dynamic knowledge graph, recommending learning resources, and recording the utilization rate of learning resources;

[0011] Generating a comprehensive learning status of students based on the learning status features and the utilization rate of learning resources;

[0012] Generating teaching feedback according to the comprehensive learning status.

[0013] As a preferred embodiment of the teaching information processing method based on electrical automation control of the present invention, wherein: the multimodal teaching information includes operation data, measurement data, analysis results, experimental report content, homework completion status, answer records, learning notes, learning duration, and learning resource access frequency of students in the experiments and simulation operations of the electrical automation control course;

[0014] The preprocessing includes data cleaning, denoising, formatting, and spatio-temporal alignment.

[0015] As a preferred embodiment of the teaching information processing method based on electrical automation control of the present invention, wherein: using the preprocessed multimodal teaching information, extract and fuse features to generate learning status features, and the specific steps are as follows.

[0016] Use a convolutional neural network to extract features from the operation data, measurement data, and analysis results in the experiments and simulation operations to obtain behavior features;

[0017] Analyze the experimental reports, homework, and learning notes through the BERT natural language processing method to extract text features;

[0018] Fuse the behavior features and text features through a multimodal attention mechanism. Before fusion, it is necessary to calculate the attention weights, and the calculation method is expressed as,

[0019]

[0020] where, α b→t is the attention weight of the behavior features to the text features, softmax is a function that converts the input vector into a probability distribution, F' b is the behavior feature, F' t is the text feature, W Q is the pre-linear transformation matrix, W K is the post-linear transformation matrix, α t→b is the attention weight of the text features to the behavior features, is the normalization factor;

[0021] Fuse the behavior features and text features through a multimodal attention mechanism to obtain the learning status features of the students, expressed as,

[0022] F s = ReLU(α b→t F' t + α t→b F' b );

[0023] where, F s is the learning status feature of the student, ReLU is the rectified linear unit function, αb→t is the attention weight of the behavioral feature to the text feature, F' t is the text feature, α t→b is the attention weight of the text feature to the behavioral feature, F' b is the behavioral feature.

[0024] As a preferred solution of the teaching information processing method based on electrical automation control described in the present invention, wherein: a knowledge graph is constructed and dynamically adjusted by using learning state features to obtain a dynamic knowledge graph, and the specific steps are as follows.

[0025] According to the structure of the knowledge points of the electrical automation control course, a knowledge graph with knowledge point nodes, node weights, and association relationships between nodes is constructed;

[0026] The mastery of each knowledge point by students is analyzed through learning state features, and the weights of the knowledge point nodes in the knowledge graph are initialized;

[0027] According to the learning state features of students, the weights of each knowledge point node in the knowledge graph are adjusted in real time, expressed as

[0028]

[0029] where m i (t) is the weight of the i-th knowledge point node at time t, m i (t - 1) is the weight of the i-th knowledge point node at time t - 1, e is the base of the natural logarithm, η is the decay rate parameter, t is the time, λ is the proportionality factor for weight update, ReLU is the rectified linear unit function, is the learning state feature vector at time t, and is the weight vector related to the i-th knowledge point;

[0030] According to the mastery of knowledge points, the association strength between each knowledge point node is dynamically adjusted, expressed as

[0031]

[0032] where is the association strength between knowledge point nodes i and j at time t, is the initial association strength, tanh is the hyperbolic tangent function, Δm i is the weight change amount of the i-th knowledge point node, Δm j is the weight change amount of the j-th knowledge point node, d ij is the distance between knowledge point nodes i and j at time t, and β is the adjustment parameter;

[0033] The adjusted weights and association relationships of the knowledge point nodes are integrated to generate a dynamic knowledge graph that reflects the learning state of students in real time.

[0034] As a preferred solution of the teaching information processing method based on electrical automation control according to the present invention, wherein: generate a personalized learning path based on a dynamic knowledge graph, recommend learning resources, and record the utilization rate of learning resources. The specific steps are as follows:

[0035] Referring to the node association relationship in the knowledge graph, use the adjacency matrix traversal algorithm to locate the knowledge points associated with low-weight nodes as review content, and generate the conditions for learning path planning;

[0036] Use the Dijkstra shortest path algorithm and according to the learning path planning conditions, generate a personalized learning path;

[0037] Analyze the learning needs of each knowledge point in the personalized learning path, and use the content-based collaborative filtering recommendation algorithm according to the learning needs to match and adapt learning resources from the teaching resource library;

[0038] Record and analyze the access frequency, learning duration, and completion degree of students to the recommended resources to obtain the resource utilization rate.

[0039] As a preferred solution of the teaching information processing method based on electrical automation control according to the present invention, wherein: generate teaching feedback according to the comprehensive learning status. The specific steps are as follows:

[0040] According to the comprehensive characteristics of knowledge point mastery and resource utilization rate, use the weight allocation algorithm to calculate the overall learning effect of students;

[0041] Use the association relationship between knowledge point nodes in the dynamic knowledge graph to analyze the contribution degree of different knowledge points to the overall learning effect;

[0042] Determine the priority of key knowledge points through the structure of the knowledge graph, and summarize the student's mastery of knowledge points according to the extracted weight data of knowledge point nodes and the priority of knowledge points to generate the overall learning effect of students;

[0043] According to the distribution of knowledge point mastery in the comprehensive learning status, identify the knowledge points that students have not mastered and have poorly mastered;

[0044] Based on the identified knowledge points that students have not mastered and have poorly mastered, push learning resources through a dynamic recommendation algorithm;

[0045] After the push is completed, re-collect and feedback the learning data to update the dynamic knowledge graph and personalized learning path.

[0046] In a second aspect, the present invention provides a teaching information processing system based on electrical automation control, including an information collection module, a feature extraction module, a knowledge graph module, a learning resource module, a learning status module, and a feedback module;

[0047] The information collection module is used to collect multi-modal teaching information and perform preprocessing;

[0048] The feature extraction module is used to extract and fuse features from the preprocessed multi-modal teaching information to generate learning status features;

[0049] The knowledge graph module is used to construct and dynamically adjust a knowledge graph using the learning status features to obtain a dynamic knowledge graph;

[0050] The learning resource module is used to generate a personalized learning path based on the dynamic knowledge graph, recommend learning resources, and record the utilization rate of learning resources;

[0051] The learning status module is used to generate a comprehensive learning status of students based on the learning status features and the utilization rate of learning resources;

[0052] The feedback optimization module is used to generate teaching feedback according to the comprehensive learning status.

[0053] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the teaching information processing method based on electrical automation control as described in the first aspect of the present invention is implemented.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the teaching information processing method based on electrical automation control as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of the present invention are as follows: By constructing a dynamic knowledge graph, combining the learning status features of students and the utilization rate of learning resources, the present invention dynamically evaluates the students' mastery of knowledge points, generates a personalized learning path, and pushes suitable learning resources. The present invention can update the weights and association relationships of each knowledge point node in the knowledge graph in real time, reflect the learning progress and weak links of students, so as to dynamically adjust teaching strategies, improve the pertinence and personalization of teaching, construct a learning path exclusive to students through the dynamic knowledge graph, and optimize learning efficiency. Provide a real-time feedback mechanism to help students quickly discover weak points in learning, and improve the mastery of knowledge points through accurate recommendation of suitable resources. Realize the closed-loop management of learning data, and continuously optimize teaching resources and path planning by collecting, analyzing, and feeding back students' learning data. Description of the Drawings

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0057] Figure 1 It is a flowchart of the teaching information processing method based on electrical automation control in Embodiment 1.

[0058] Figure 2 It is a module diagram of the teaching information processing system based on electrical automation control in Embodiment 1. Detailed implementation manners

[0059] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed implementation manners of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0060] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0062] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a teaching information processing method based on electrical automation control, including the following steps:

[0063] S1. Collect multimodal teaching information and perform preprocessing, including the following steps:

[0064] Multimodal teaching information includes operation data, measurement data, analysis results, experimental report content, homework completion status, answer records, learning notes, learning duration, and learning resource access frequency of students in the experiments and simulation operations of the electrical automation control course. Specifically, the operation sequence, operation parameters, and measurement data during the experiments and simulation operations of the electrical automation control course are collected in real time. The report text and homework answers submitted by students. The answer record is the answer data of students in online exercises and exams; the learning resource access record is the frequency and duration of students' access to learning resources (such as e-textbooks, video explanations); the learning note is the content of the notes taken by students during the learning process.

[0065] Preprocessing includes data cleaning, denoising, formatting, and spatio-temporal alignment. Specifically, incomplete, duplicate, or invalid records are removed, such as missing steps or outliers in experimental operations; the median filtering method is used to smooth the noise in the measurement data; the experimental operation data, resource access records, etc. are aligned according to timestamps to ensure that different modal information is analyzed on the same time axis.

[0066] It should be noted that the preprocessing method adopted can effectively improve the quality of multimodal data, ensure the consistency of different types of data in time and space, and thus lay a foundation for subsequent feature extraction and fusion. By comprehensively collecting and standardizing multimodal teaching information, the present invention can accurately capture students' learning behaviors and states, providing high-quality data support for the construction of a dynamic knowledge graph.

[0067] S2. Using the preprocessed multimodal teaching information, extract and fuse features to generate learning state features, including the following steps.

[0068] Use a convolutional neural network to extract features from the operation data, measurement data, and analysis results in the experiments and simulation operations to obtain behavioral features. Specifically, segment the operation sequence and extract the key operation features of each segment (such as operation duration, parameter change trend); capture local patterns in the measurement data and extract the operation accuracy and consistency features of students in the experiment; classify the analysis results to identify whether students have completed correct conclusions in the experiment.

[0069] Analyze the experimental reports, homework, and learning notes through the BERT natural language processing method (Bidirectional Encoder Representations from Transformers) to extract text features. Specifically, tokenize the text and input it into the BERT model to extract semantic embedding representations; extract the semantic features of key sentences in students' reports, such as experimental purposes, methods, and conclusions.

[0070] Fuse the behavioral features and text features through a multimodal attention mechanism. Before fusion, it is necessary to calculate the attention weights, and the calculation method is expressed as

[0071]

[0072]

[0073] Among them, α b→t is the attention weight of the behavioral feature to the text feature, softmax is a function that converts the input vector into a probability distribution, and F' b is the behavioral feature, and F' t is the text feature, W Q is the pre - linear transformation matrix, and W K is the post - linear transformation matrix, and α t→b is the attention weight of the text feature to the behavioral feature. is the normalization factor.

[0074] The behavioral feature and the text feature are fused through the multi - modal attention mechanism to obtain the learning state feature of the student, which is expressed as

[0075] F s = ReLU(α b→t F' t + α t→b F' b );

[0076] Among them, F s is the learning state feature of the student, ReLU is the rectified linear unit function, α b→t is the attention weight of the behavioral feature to the text feature, F' t is the text feature, α t→b is the attention weight of the text feature to the behavioral feature, and F' b is the behavioral feature.

[0077] It should be noted that using a convolutional neural network to extract behavioral features can effectively capture the behavioral patterns of students in experiments and simulation operations; using BERT to extract text features can deeply mine the semantic information in students' learning texts; implementing feature fusion through the multi - modal attention mechanism can comprehensively reflect the learning state of students. By fusing behavioral features and text features, the present invention can more comprehensively and accurately depict the learning state of students, providing a reliable basis for the initialization and update of the dynamic knowledge graph.

[0078] S3. Construct and dynamically adjust the knowledge graph using the learning state feature to obtain a dynamic knowledge graph, including the following steps

[0079] Construct a knowledge graph with knowledge point nodes, node weights, and the association relationships between nodes according to the structure of the knowledge points in the electrical automation control course. Specifically, define each knowledge point in the course as a node of the knowledge graph; establish edges between nodes according to the logical relationships (such as prerequisite relationships and correlation degrees) between knowledge points; set an initial weight for each knowledge point node to reflect its importance or fundamentality.

[0080] Analyze the students' mastery of each knowledge point through learning state characteristics, and initialize the weights of the knowledge point nodes in the knowledge graph.

[0081] Specifically, according to the students' learning state characteristics, adjust the weights of each knowledge point node in the knowledge graph in real time, expressed as

[0082]

[0083] where m i (t) is the weight of the i-th knowledge point node at time t, m i (t - 1) is the weight of the i-th knowledge point node at time t - 1, e is the base of the natural logarithm, η is the decay rate parameter, t is the time, λ is the weight update scale factor, ReLU is the rectified linear unit function, is the learning state feature vector at time t, and is the weight vector related to the i-th knowledge point.

[0084] According to the mastery of knowledge points, dynamically adjust the association strength between each knowledge point node, expressed as

[0085]

[0086] where is the association strength between knowledge point node i and knowledge point j at time t, is the initial association strength, tanh is the hyperbolic tangent function, Δm i is the weight change of the i-th knowledge point node, Δm j is the weight change of the j-th knowledge point node, d ij is the distance between knowledge point node i and knowledge point node j at time t, and β is the adjustment parameter.

[0087] Integrate the adjusted weights and association relationships of the knowledge point nodes to generate a dynamic knowledge graph that reflects the students' learning state in real time. Specifically, if a student understands the relevant knowledge points after learning a certain knowledge point, then enhance the association strength between the two; if a student's mastery of a certain knowledge point is insufficient, then weaken its association strength with the subsequent knowledge points.

[0088] It should be noted that by dynamically adjusting the node weights and edge weights in the knowledge graph, the knowledge graph can reflect the learning status and knowledge mastery of students in real time. The construction and adjustment of the dynamic knowledge graph make the personalized learning path of students more accurate, and at the same time can help teachers understand the learning weaknesses of students in real time.

[0089] S4. Generate a personalized learning path based on the dynamic knowledge graph, recommend learning resources, and record the utilization rate of learning resources, including the following steps:

[0090] Referring to the node association relationship in the knowledge graph, use the adjacency matrix traversal algorithm to locate the knowledge points associated with the low-weight nodes as the review content, and generate the conditions for learning path planning. Specifically, referring to the association relationship between knowledge points in the dynamic knowledge graph, use the adjacency matrix traversal algorithm to locate the knowledge points associated with the low-weight nodes; regard the low-weight knowledge points and their associated knowledge points as the key content for review and learning, and generate the learning path planning conditions.

[0091] Use the Dijkstra shortest path algorithm and generate a personalized learning path according to the learning path planning conditions. Specifically, use the Dijkstra shortest path algorithm to generate the personalized best learning path for each student according to the path planning conditions, ensuring the shortest path and covering all knowledge points to be learned.

[0092] Analyze the learning needs of each knowledge point in the personalized learning path, and use the content-based collaborative filtering recommendation algorithm to match and adapt learning resources from the teaching resource library according to the learning needs. Specifically, analyze the learning needs of each knowledge point in the personalized learning path (such as experimental operation guides, theoretical explanation videos); use the content-based collaborative filtering recommendation algorithm to match and adapt learning resources from the teaching resource library, and sort them according to the relevance and quality of the resources.

[0093] Record and analyze the access frequency, learning duration, and completion rate of students for the recommended resources to obtain the resource utilization rate. Specifically, monitor the usage of recommended resources by students, including access frequency, learning duration, and completion rate; evaluate the utilization rate of learning resources according to the monitoring data, and provide a basis for subsequent learning status evaluation.

[0094] It should be noted that by combining the dynamic knowledge graph and the recommendation algorithm, a personalized learning path can be intelligently generated and learning resources can be accurately recommended. By optimizing the learning path and resource recommendation, the present invention can significantly improve the learning efficiency and knowledge mastery of students.

[0095] S5. Generate the comprehensive learning status of students based on the learning status characteristics and the utilization rate of learning resources, including the following steps:

[0096] For each knowledge point node in the dynamic knowledge graph, extract the weight of the current knowledge point node. Specifically, extract the current weight of each knowledge point from the dynamic knowledge graph. The weight reflects the student's mastery of the knowledge point. A knowledge point with a higher weight indicates that the student has a better mastery, while a knowledge point with a lower weight indicates a poorer mastery. The weight is derived from the real-time adjustment of the dynamic knowledge graph, which has comprehensively considered the student's learning behavior and status.

[0097] Utilize the weight of the current knowledge point node to analyze the behavioral characteristics in the student's learning state and obtain the mastery degree index of the knowledge point by the student during the current learning cycle. Specifically, the behavioral characteristics include the operation accuracy, experiment completion time, and resource usage frequency of the student in experiments and simulation operations. For each knowledge point node, determine whether the student has completed the relevant experimental tasks or learning tasks, and the completion situation will directly affect the mastery degree index of this knowledge point.

[0098] According to the learning resource utilization rate, correct the mastery degree index of the knowledge point and combine it with the learning state characteristics to obtain the student's comprehensive learning state. Specifically, if the student frequently accesses the resources related to a certain knowledge point and completes the learning, then increase the mastery degree index of this knowledge point; if the student uses fewer resources for a certain knowledge point or fails to complete the relevant tasks, then reduce its mastery degree index. The comprehensive learning state reflects the overall mastery of most knowledge points in the course by the student. The comprehensive learning state can be qualitatively expressed as categories such as "excellent", "good", "needs improvement", etc., or quantitatively represented as a comprehensive score or percentage.

[0099] It should be noted that by extracting the weights of knowledge point nodes, analyzing learning behavioral characteristics, and correcting the mastery degree in combination with resource utilization rate, the student's learning state can be comprehensively reflected. By generating the comprehensive learning state, the overall learning effect of the student can be accurately evaluated, providing a reliable basis for subsequent teaching feedback and strategy optimization.

[0100] S6. Generate teaching feedback according to the comprehensive learning state, including the following steps:

[0101] According to the comprehensive characteristics of the knowledge point mastery degree and resource utilization rate, use the weight distribution algorithm to calculate the overall learning effect of the student.

[0102] Specifically, utilize the association relationship between knowledge point nodes in the dynamic knowledge graph to analyze the contribution degree of different knowledge points to the overall learning effect.

[0103] Specifically, determine the priority of key knowledge points through the structure of the knowledge graph, and summarize the students' mastery of knowledge points based on the weight data of the extracted knowledge point nodes and the priority of knowledge points to generate the overall learning effect of the students. Specifically, extract the weight data of knowledge points, analyze the contribution of different knowledge points to the overall learning effect; determine the priority of key knowledge points, for example, the contribution of basic knowledge points is higher than that of advanced knowledge points; summarize the students' mastery of knowledge points to generate an evaluation result of the students' overall learning effect (such as "excellent", "good", "need to be strengthened").

[0104] According to the distribution of the mastery degree of knowledge points in the comprehensive learning status, identify the knowledge points that students have not mastered and have poor mastery. Specifically, find out the knowledge points with lower weights or weaker association strengths of knowledge point nodes; analyze whether these knowledge points are key nodes in the learning path, and prioritize solving the learning problems of key knowledge points.

[0105] Based on the identified knowledge points that students have not mastered and have poor mastery, push learning resources through a dynamic recommendation algorithm. Specifically, recommend relevant experimental guides, video explanations, and exercise sets for the knowledge points that have not been mastered and have poor mastery; prioritize recommending high-quality resources according to the adaptability and historical utilization rate of the resources.

[0106] After the push is completed, re-collect and feedback the learning data to update the dynamic knowledge graph and the personalized learning path. Specifically, update the weights of knowledge point nodes in the knowledge graph to reflect the latest mastery of knowledge points by students; adjust the association strength between knowledge points to optimize the logical relationship of knowledge points; according to the updated dynamic knowledge graph, re-plan the personalized learning path of students to ensure that the learning path can cover all unmastered knowledge points and meet the review requirements.

[0107] It should be noted that by generating targeted teaching feedback and optimizing teaching strategies, the weak links in students' learning can be effectively solved; by dynamically recommending learning resources and updating the knowledge graph, a closed-loop learning optimization system is formed. By generating teaching feedback and optimizing teaching strategies, the learning effect and teaching efficiency of students can be significantly improved, and at the same time, strong support for personalized teaching can be provided.

[0108] This embodiment also provides a teaching information processing system based on electrical automation control, including: an information collection module, a feature extraction module, a knowledge graph module, a learning resource module, a learning status module, and a feedback module;

[0109] An information collection module, which is used to collect multi-modal teaching information and perform preprocessing; a feature extraction module, which is used to extract and fuse features by using the preprocessed multi-modal teaching information to generate learning status features; a knowledge graph module, which is used to construct and dynamically adjust a knowledge graph by using the learning status features to obtain a dynamic knowledge graph; a learning resource module, which is used to generate a personalized learning path based on the dynamic knowledge graph, recommend learning resources, and record the utilization rate of learning resources; a learning status module, which is used to generate the comprehensive learning status of students based on the learning status features and the utilization rate of learning resources; a feedback optimization module, which is used to generate teaching feedback according to the comprehensive learning status.

[0110] This embodiment also provides a computer device, which is applicable to the situation of the teaching information processing method based on electrical automation control, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the teaching information processing method based on electrical automation control as proposed in the above embodiment.

[0111] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0112] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the teaching information processing method based on electrical automation control as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disc.

[0113] In summary, the present invention dynamically evaluates students' mastery of knowledge points by constructing a dynamic knowledge graph, combining students' learning status characteristics and learning resource utilization rate, generates personalized learning paths and pushes adapted learning resources. The present invention can update the weights and association relationships of each knowledge point node in the knowledge graph in real time, reflect students' learning progress and weak links, thereby dynamically adjusting teaching strategies, improving the pertinence and personalization of teaching, constructing students' exclusive learning paths through the dynamic knowledge graph, and optimizing learning efficiency. Provide a real-time feedback mechanism to help students quickly identify weak points in learning and improve their mastery of knowledge points through accurate recommendation of adapted resources. Implement closed-loop management of learning data, continuously optimize teaching resources and path planning by collecting, analyzing and feeding back students' learning data.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A teaching information processing method based on electrical automation control, characterized in that: include, Collect multimodal teaching information and perform preprocessing; Using the preprocessed multimodal teaching information, features are extracted and fused to generate learning state features; Use learning state features to construct and dynamically adjust the knowledge graph to obtain a dynamic knowledge graph; Generate personalized learning paths based on dynamic knowledge graphs, recommend learning resources, and record the utilization rate of learning resources; Generate students' comprehensive learning status based on learning status characteristics and learning resource utilization; Generate teaching feedback based on the comprehensive learning status.

2. The teaching information processing method based on electrical automation control according to claim 1, characterized in that: The multimodal teaching information includes operation data, measurement data and analysis results, experimental report content, homework completion status, answer records, study notes, study time and learning resource access frequency of students in experiments and simulation operations of electrical automation control courses; The preprocessing includes data cleaning, denoising, formatting and spatiotemporal alignment.

3. The teaching information processing method based on electrical automation control as claimed in claim 2, characterized in that: Using the preprocessed multimodal teaching information, extract and fuse features to generate learning state features. The specific steps are as follows: Use convolutional neural networks to extract features from the operation data, measurement data, and analysis results in the experimental and simulation operations to obtain behavioral features; Analyze lab reports, homework, and study notes using the BERT natural language processing method to extract text features; The behavior features and text features are fused through the multimodal attention mechanism. Before fusion, the attention weight needs to be calculated. The calculation method is expressed as: Among them, α b→t is the attention weight of the behavior feature to the text feature, softmax is the function that converts the input vector into a probability distribution, and F' b is the behavioral characteristic, F' t is the text feature, W Q is the pre-linear transformation matrix, W K is the post linear transformation matrix, α t→b is the attention weight of text features on behavioral features, is the standardization factor; The behavior features and text features are integrated through the multimodal attention mechanism to obtain the student's learning state features, which are expressed as: F s =ReLU(α b→t F' t +a t→b F' b ); Among them, F s is the learning state feature of the student, ReLU is the linear rectification function, α b→t is the attention weight of the behavior feature to the text feature, F' t is the text feature, α t→b is the attention weight of text features to behavioral features, F' b For behavioral characteristics.

4. The teaching information processing method based on electrical automation control as claimed in claim 3, characterized in that: Use learning state features to construct and dynamically adjust the knowledge graph to obtain a dynamic knowledge graph. The specific steps are as follows: According to the structure of knowledge points in the electrical automation control course, a knowledge graph with knowledge point nodes, node weights and association relationships between nodes is constructed; Analyze students’ mastery of each knowledge point through learning status characteristics and initialize the weights of knowledge point nodes in the knowledge graph; According to the characteristics of students' learning status, the weight of each knowledge point node in the knowledge graph is adjusted in real time, expressed as: Among them, m i (t) is the weight of the i-th knowledge point node at time t, m i (t-1) is the weight of the i-th knowledge point node at time t-1, e is the base of the natural logarithm, η is the decay rate parameter, t is time, λ is the scale factor for weight update, ReLU is the linear rectification function, is the learning state feature vector at time t, is the weight vector associated with the i-th knowledge point; According to the mastery of knowledge points, the association strength between each knowledge point node is dynamically adjusted, which is expressed as: in, is the association strength between knowledge point node i and knowledge point j at time t, is the initial correlation strength, tanh is the hyperbolic tangent function, Δm i is the weight change of the i-th knowledge point node, Δm j is the weight change of the jth knowledge point node, d ij is the distance between knowledge point node i and knowledge point node j at time t, and β is the adjustment parameter; Integrate the adjusted weights and associations of knowledge point nodes to generate a dynamic knowledge graph that reflects students' learning status in real time.

5. The teaching information processing method based on electrical automation control as claimed in claim 4, characterized in that: Generate personalized learning paths based on dynamic knowledge graphs, recommend learning resources, and record the utilization rate of learning resources. The specific steps are as follows: Referring to the node association relationship in the knowledge graph, the adjacency matrix traversal algorithm is used to locate the knowledge points associated with low-weight nodes as review content, and the conditions for learning path planning are generated; Generate personalized learning paths using Dijkstra's shortest path algorithm and according to the learning path planning conditions; Analyze the learning needs of each knowledge point in the personalized learning path, and use the content-based collaborative filtering recommendation algorithm to match appropriate learning resources from the teaching resource library according to the learning needs; Record and analyze students’ access frequency, study duration, and completion rate of recommended resources to obtain resource utilization rate.

6. The teaching information processing method based on electrical automation control as claimed in claim 5, characterized in that: Based on the learning status characteristics and learning resource utilization rate, the comprehensive learning status of students is generated. The specific steps are as follows: For each knowledge point node in the dynamic knowledge graph, extract the weight of the current knowledge point node; Using the weight of the current knowledge point node, analyze the behavioral characteristics of the student's learning state and obtain the student's mastery of the knowledge point in the current learning cycle; According to the utilization rate of learning resources, the knowledge point mastery index is revised and combined with the learning status characteristics to obtain the students' comprehensive learning status.

7. The teaching information processing method based on electrical automation control as claimed in claim 6, characterized in that: Generate teaching feedback based on the comprehensive learning status. The specific steps are as follows: According to the comprehensive characteristics of knowledge point mastery and resource utilization, the weight distribution algorithm is used to calculate the overall learning effect of students; Utilize the correlation between knowledge point nodes in the dynamic knowledge graph to analyze the contribution of different knowledge points to the overall learning effect; Determine the priority of key knowledge points through the structure of the knowledge graph, and summarize the students' mastery of knowledge points based on the weight data of the extracted knowledge point nodes and the priority of the knowledge points to generate the students' overall learning effect; According to the distribution of knowledge points mastery in the comprehensive learning status, identify the knowledge points that students have not mastered or have mastered poorly; Based on the knowledge points that students have not mastered or have mastered poorly, learning resources are pushed through a dynamic recommendation algorithm; After the push is completed, the learning data will be collected again and fed back to update the dynamic knowledge graph and personalized learning path.

8. A teaching information processing system based on electrical automation control, based on the teaching information processing method based on electrical automation control according to any one of claims 1 to 7, characterized in that: Including information collection module, feature extraction module, knowledge graph module, learning resource module, learning status module and feedback module; The information acquisition module is used to collect multimodal teaching information and perform preprocessing; The feature extraction module is used to extract and fuse features using the pre-processed multimodal teaching information to generate learning state features; The knowledge graph module is used to construct and dynamically adjust the knowledge graph using learning state features to obtain a dynamic knowledge graph; The learning resource module is used to generate personalized learning paths based on the dynamic knowledge graph, recommend learning resources, and record the utilization rate of learning resources; The learning status module is used to generate a comprehensive learning status of the student based on the learning status characteristics and the learning resource utilization rate; The feedback optimization module is used to generate teaching feedback according to the comprehensive learning status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the teaching information processing method based on electrical automation control described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the teaching information processing method based on electrical automation control described in any one of claims 1 to 7 are implemented.

Images