Tumor auxiliary memory system and device based on Ebbinghaus forgetting curve

Abstract

The invention discloses a tumor auxiliary memory system and device based on an Ebbinghaus forgetting curve, and the system comprises a knowledge point determination module which determines the knowledge points of tumor teaching; the knowledge points comprise tumor classification, clinical manifestation and treatment methods; the review module is used for determining review time based on the Ebbinghaus forgetting curve principle in combination with the information of the knowledge points and generating a review plan corresponding to the review time; the reviewing time is the first day, the second day, the fourth day, the seventh day or the fifteenth day; the mind map module is used for generating a tumor-centered mind map according to the knowledge points and the review plan; the mind map comprises a plurality of secondary child nodes comprising review plans, and each secondary child node is used for refining and classifying knowledge points. According to the invention, in combination with the memory rule of the Ebbinghaus forgetting curve, students are assisted to memorize tumor knowledge, and the memory effect and learning efficiency of the students are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical education technology, and in particular to a tumor auxiliary memory system and device based on the Ebbinghaus forgetting curve. Background Art

[0002] The forgetting curve proposed by British scholar Ebbinghaus is a recognized scientific theory about memory and forgetting. The Ebbinghaus memory method based on this theory is widely used in the field of education. The course content of lung malignant tumors in oncology nursing is complex and has many knowledge points, which requires a lot of memory to consolidate and master. Therefore, the memory method based on the Ebbinghaus forgetting curve should also be applied in oncology nursing practice.

[0003] Mind maps, also known as mind maps, can show the relationship between different topics in the form of pictures and text, linking topic keywords with colors and images, helping us organize information, better analyze, understand, integrate, remember and imagine, and thus help people understand and remember relevant knowledge more intuitively. By using mind maps for knowledge learning, it is helpful to form a knowledge framework, build a logical system, establish clinical thinking, sort out content, and highlight the theme and level of knowledge.

[0004] Oncology is one of the key departments of the hospital. Tumor diseases are complex and diverse, especially the knowledge of lung malignant tumors, which is relatively abstract. Clinical practice learning is the initial stage of applying the theoretical knowledge learned by nursing students during school to clinical practice. In the traditional teaching practice of oncology, the teaching model has certain limitations. The rote or cramming short-term memory learning method will not only dampen the enthusiasm for learning and reduce learning efficiency, but also make it difficult to form a knowledge system. There are many teaching contents, and nursing students are in a state of passive learning during the internship. They lack organization or rote learning, lack the spirit of integration, the ability of independent learning, and the spirit of scientific research innovation.

[0005] In summary, there are problems such as fast forgetting, complex knowledge points, and low learning efficiency in the current teaching of lung malignant tumors. How to combine the memory law of the Ebbinghaus forgetting curve and use the intuitive advantages of mind mapping to improve teaching effects has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] According to one aspect of the present invention, a tumor auxiliary memory system and device based on the Ebbinghaus forgetting curve is provided, which assists students in memorizing tumor knowledge by combining the memory rules of the Ebbinghaus forgetting curve, thereby improving students' memory effect and learning efficiency.

[0007] In a first aspect, the present application provides a tumor auxiliary memory system based on the Ebbinghaus forgetting curve, comprising:

[0008] A knowledge point determination module determines the knowledge points of tumor teaching; the knowledge points include tumor classification, clinical manifestations, and treatment methods;

[0009] A review module, based on the principle of the Ebbinghaus forgetting curve, combines the information of the knowledge points to determine the review time, and generates a review plan corresponding to the review time; the review time includes the first day, the second day, the fourth day, the seventh day or the fifteenth day;

[0010] A mind map module generates a tumor-centered mind map based on the knowledge points and the review plan; the mind map includes a number of secondary sub-nodes including the review plan, and each of the secondary sub-nodes is used to refine and classify the knowledge points.

[0011] In some embodiments, it further includes a cloud, a teacher terminal and a student terminal, wherein the cloud includes a database storing the knowledge points, mind maps and review plans;

[0012] The student terminal is used to obtain learning behavior information and learning progress, and manage mind maps and review plans;

[0013] The teacher terminal is used to view the mind map module of the student terminal, obtain the learning behavior data of the student terminal, and adjust the teaching plan according to the learning behavior data.

[0014] In some embodiments, the cloud includes an API interface connected to an AI auxiliary module, and the AI ​​auxiliary module generates recommended teaching courses and learning status reports based on learning behavior data, and interacts with the student terminal or teacher terminal in natural language.

[0015] In some embodiments, the mind mapping module performs time planning through a Gantt chart according to the time review interval and the priority of the task point, and dynamically adjusts the task bar and review time node in the Gantt chart according to the learning behavior data; the horizontal axis of the Gantt chart is divided into days, weeks, and months, and the vertical axis represents the review task bar, which includes knowledge points and review time nodes.

[0016] In some embodiments, the student terminal further includes a test module, which generates corresponding test questions according to the review plan and the mind map, and obtains test data; each of the test questions includes basic questions, extended questions and comprehensive questions of knowledge points.

[0017] In some implementations, a learning achievement feedback module is further included, which establishes a learning scoring system based on the test module and the knowledge point module, and analyzes the learning achievements through a learning achievement feedback model based on the learning behavior data of the student terminal to determine the degree of mastery of the knowledge points;

[0018] The learning behavior data includes knowledge point learning data, test data and review data; the knowledge point learning data includes login frequency, online learning time, video viewing records, number of visits, and visit duration; the test data includes test scores, test accuracy, error types, and test time; the review data includes review frequency, review content, and review effect.

[0019] In some embodiments, the learning outcome feedback model is used to quantitatively evaluate the learning and memory effects, and is calculated using the following formula:

[0020] Learning and memory effect score = (correct rate × answering speed coefficient × review frequency coefficient) / total test time;

[0021] Among them: the correct rate is the correct rate of students' answers, that is, the ratio of the number of questions answered correctly to the total number of questions; Correct rate = (number of questions answered correctly / total number of questions) × 100%;

[0022] The answering speed coefficient is used to reflect the speed at which students complete questions within the specified time and is determined based on the answering speed;

[0023] The review frequency coefficient is used to reflect the students’ review of the learned content and is determined based on the number of reviews;

[0024] The total test time is the total time it takes students to complete all questions, in hours.

[0025] In some embodiments, the mind map module generates a mind map by combining color marking and Gantt chart; the color marking is related to the mastery of knowledge points in the learning outcome feedback module; the color marking includes the color, font, and shading of the mind map.

[0026] In a second aspect, the present application provides a tumor auxiliary memory device based on the Ebbinghaus forgetting curve, comprising:

[0027] Memory;

[0028] A processor and a computer program stored in a memory and executable on the processor, wherein when the processor executes the computer program, a tumor auxiliary memory system based on the Ebbinghaus forgetting curve as described in any one of the above items is implemented.

[0029] The present application provides a tumor auxiliary memory system and device based on the Ebbinghaus forgetting curve. By combining the Ebbinghaus forgetting curve and the mind map for review, students can remember the knowledge points more deeply and lastingly, and can recall the knowledge learned faster in the exam, and can apply the learned content more accurately in clinical practice. In addition, due to the visualization characteristics of the mind map, students can also more intuitively understand the connection and difference between knowledge, which helps to form a systematic knowledge system, which can not only improve students' learning efficiency, but also help them establish a systematic oncology knowledge system, laying a solid foundation for future clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a module of a tumor auxiliary memory system based on the Ebbinghaus forgetting curve provided by the present invention;

[0031] Figure 2 A mind map schematic diagram of a tumor auxiliary memory system based on the Ebbinghaus forgetting curve provided by the present invention;

[0032] Figure 3 This is a schematic diagram of the structure of a tumor auxiliary memory system based on the Ebbinghaus forgetting curve provided by the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0034] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products or apparatus.

[0035] like Figure 1 As shown, the present application provides a tumor auxiliary memory system based on the Ebbinghaus forgetting curve, which combines the memory rules of the Ebbinghaus forgetting curve to assist students in memorizing tumor knowledge and improve students' memory effect and learning efficiency.

[0036] Specifically, the system includes: a knowledge point determination module, a review module and a mind map module. The knowledge point determination module is used to determine the knowledge points of tumor teaching; the knowledge points include the classification, clinical manifestations, and treatment methods of tumors. The review module determines the review time based on the principle of the Ebbinghaus forgetting curve and the information of the knowledge points, and generates a review plan corresponding to the review time; the review time includes the first day, the second day, the fourth day, the seventh day or the fifteenth day. The mind map module generates a tumor-centered mind map based on the knowledge points and the review plan; the mind map includes several secondary sub-nodes including a review plan, and each of the secondary sub-nodes is used to refine and classify the knowledge points.

[0037] The knowledge point determination module clarifies the teaching objectives and determines the knowledge points of tumor teaching, including tumor classification, clinical manifestations, diagnostic methods, treatment methods, etc. Knowledge points can be presented in various forms such as video, animation, text or images, such as tumor video courses, tumor case explanations, etc., to meet the needs of users in different aspects. At the same time, through various forms, it is also convenient for users to familiarize themselves with, understand and remember the relevant knowledge points of tumors.

[0038] The review module determines the review time based on the principle of the Ebbinghaus forgetting curve and the information of the knowledge points, and generates a review plan corresponding to the review time; the review time includes the first day, the second day, the fourth day, the seventh day or the fifteenth day. Combined with the Ebbinghaus forgetting curve for memory learning, according to the principle of the Ebbinghaus forgetting curve, a reasonable review time is set in conjunction with the memory cycle. For example, after learning a certain chapter or a certain knowledge point, the review content of the day can be set in the second memory cycle, the third memory cycle, that is, 30 minutes, 12 hours, and the fourth memory cycle, the fifth memory cycle, the sixth memory cycle, the seventh memory cycle, the eighth memory cycle, that is, the first day, the second day, the fourth day, the seventh day or the fifteenth day, etc., to ensure that the knowledge points can be effectively consolidated.

[0039] like Figure 2 As shown, the mind map module generates a tumor-centered mind map based on the knowledge points and the review plan; the mind map includes several secondary sub-nodes including the review plan, and each of the secondary sub-nodes is used to refine and classify the knowledge points. The review date or review cycle is marked near the secondary sub-nodes of the mind map, so that students can clearly see their learning progress and review plan, which is convenient for managing time and planning learning.

[0040] Exemplarily, the mind map is centered on "lung cancer", which is provided with multiple secondary sub-nodes, including first-level sub-nodes, such as "classification, clinical manifestations, diagnostic methods, treatment methods of tumors", etc., and detailed second-level sub-nodes and third-level sub-nodes are continued to be added under the first-level sub-nodes to refine and classify the knowledge points. For example, under "therapeutics", second-level nodes such as "chemotherapy" and "surgical treatment" can be added, and third-level nodes such as "drug classification", "route of administration" and "side effects" can be further subdivided under "chemotherapy".

[0041] By combining the Ebbinghaus forgetting curve with mind mapping for review, students can remember knowledge points more deeply and lastingly. They can recall what they have learned faster and apply what they have learned more accurately in clinical practice. In addition, due to the visualization characteristics of mind mapping, students can also more intuitively understand the connections and differences between knowledge, which helps to form a systematic knowledge system. Through such teaching strategies, students can not only improve their learning efficiency, but also help to establish a systematic oncology knowledge system, laying a solid foundation for future clinical practice.

[0042] More, for the convenience of users, such as Figure 3 As shown, the system also has a cloud, a teacher terminal and a student terminal. The cloud includes a database for storing the knowledge points, mind maps and review plans. Other relevant knowledge points can also be stored in the cloud, and can be searched and downloaded through the cloud when the user needs other relevant knowledge points. The cloud establishes a real-time connection between the student terminal and the teacher terminal to ensure the timely transmission and exchange of information. The cloud database is used to store and manage a large amount of auxiliary teaching materials, including video courses, case explanations, etc. Teachers can upload teaching materials such as video courses and case explanations to the cloud database through the teacher terminal, so that student terminals can access and study at any time. The cloud database will ensure real-time synchronization and backup of the data to ensure that students can always access the latest teaching materials.

[0043] The student terminal is used to obtain learning behavior information and learning progress, and manage mind maps and review plans. Students can view their learning progress in real time through the student terminal, and have a comprehensive understanding and grasp of their learning situation. Students can conveniently draw, modify and organize their mind maps here. With this system, students can not only intuitively sort out the knowledge context, but also clearly understand their mastery of each knowledge point through quantitative data.

[0044] Teachers can view the learning progress or learning difficulties of students' terminals through the teacher terminal. The teacher client is mainly responsible for viewing students' mind maps and providing corresponding modification suggestions. Teachers can use the client to understand each student's mind map in detail, find problems and put forward improvement suggestions to help students better understand and master knowledge. In addition, the teacher client also has an overview of students' learning progress, allowing teachers to fully understand the learning situation of all students and provide strong data support for the adjustment and optimization of teaching plans.

[0045] This method realizes effective communication between students and teachers through the introduction of cloud, teacher terminal and student terminal, provides convenient learning and management tools, helps to improve students' learning efficiency, helps teachers to better manage and guide teaching, and provides students and teachers with an efficient learning and management platform. In this application, the teacher terminal and the student terminal are only used to illustrate their differences, and the actual application scenario is not limited by the identity of the user.

[0046] Furthermore, the secondary sub-nodes of the mind map in the mind map module are provided with hyperlinks related to the secondary sub-nodes, and the hyperlinks are used to jump to the cloud database when a click instruction is obtained to display the auxiliary teaching materials related to the secondary sub-nodes. By setting hyperlinks, users can quickly access the teaching materials related to their own learning, reduce the time consumed in searching and identifying teaching materials, improve learning efficiency, and make the learning process more intuitive and clear.

[0047] The mind map module performs time planning through a Gantt chart according to the time review interval and the priority of the task point, and dynamically adjusts the task bars and review time nodes in the Gantt chart according to the learning behavior data; the horizontal axis of the Gantt chart is divided into days, weeks, and months, and the vertical axis represents the review task bars, which include knowledge points and review time nodes.

[0048] Plan students' learning progress and review plans in the Gantt chart, add each review date or review cycle as a time node in the Gantt chart, link the time node in the Gantt chart to the corresponding mind map node, realize the association between time and knowledge points, and update the progress bar in the Gantt chart as learning progresses to indicate the students' mastery of the knowledge points.

[0049] The mind map module combines color marking and Gantt chart to generate a mind map; the color marking is related to the degree of mastery of the knowledge points in the learning outcome feedback module; the color marking includes the color, font, and shading of the mind map. Knowledge points are distinguished by color, font, or shading. For example, important or difficult-to-understand knowledge points are highlighted with bold and elongated fonts, and a light blue shading is added to the learning points. Utilize changes in color and font to visualize complex information and results, and mobilize the logic, imagination, and holistic thinking of the left brain. For students with poor abstract thinking ability, the unique "image memory" of mind maps makes it easier to remember knowledge.

[0050] Furthermore, mind maps are combined with color marking for learning and memory, using different colors to indicate the degree of students' mastery of knowledge points. For example, green indicates mastery, yellow indicates partial mastery, and red indicates non-mastery. As the number of reviews increases, the depth of the color can gradually change to indicate the student's familiarity with the knowledge point.

[0051] The student terminal is equipped with an overview of the student's learning progress, including an overall learning progress ratio chart and an individual learning progress distribution chart. The teacher's terminal automatically generates a learning progress ratio chart and an individual learning progress distribution chart based on the color of the student's terminal mark. Based on the display of the overall learning progress ratio chart, the teacher can clearly identify the student's mastery of different knowledge nodes and further view the mastery of different knowledge nodes of the same student.

[0052] The teacher terminal also has an online teaching function. According to the display of the overall learning progress ratio chart, online large classes are organized for intensive learning of knowledge points with a lower mastery ratio among students, ensuring that students can fully understand and master the key knowledge points. For knowledge points with a higher mastery ratio, for which a few students have not mastered the content, the teacher can provide one-on-one tutoring and targeted guidance and help to ensure that every student can keep up with the learning progress.

[0053] The cloud includes an API interface connected to an AI auxiliary module, and the AI ​​auxiliary module generates recommended teaching courses and learning status reports based on learning behavior data, and interacts with the student terminal or teacher terminal in natural language. The AI ​​auxiliary module can be a relatively mature AI large model at present, which queries relevant information of knowledge points through an artificial intelligence assistant, or interacts in natural language. The AI ​​auxiliary module can recommend relevant teaching materials or courses based on information such as students' learning history, interest preferences, etc. This helps students to use learning resources more efficiently and improve learning effects. Students can interact with the AI ​​auxiliary module in natural language through the user interface in the cloud, ask questions and get answers. The AI ​​auxiliary module can understand students' questions and generate clear and logically rigorous replies to help students solve doubts in learning. Moreover, students' learning data can be analyzed to provide teachers with reports and suggestions on students' learning status. This helps teachers better understand students' learning progress and difficulties, so as to adjust teaching strategies and methods.

[0054] In order to quickly detect the students' mastery of tumor-related knowledge points, the student terminal also includes a test module, which generates corresponding test questions according to the review plan and mind map, and obtains test data; each test question includes basic questions, extended questions and comprehensive questions of knowledge points. Corresponding test questions are set at different review times. For example, basic questions related to knowledge points are set on the first day, extended questions are set according to the secondary sub-nodes of the mind map on the third day, and comprehensive questions of the secondary sub-nodes of the mind map are set after one week or two weeks. It assists in memorizing knowledge points and testing the mastery of knowledge points at different times, so as to timely check for omissions and fill in the gaps.

[0055] Furthermore, the learning achievement feedback module establishes a learning scoring system based on the test module and the knowledge point module, and analyzes the learning achievements through the learning achievement feedback model based on the learning behavior data of the student terminal to determine the degree of mastery of the knowledge points. Specifically, it includes:

[0056] Collect learning behavior data of the test module and knowledge point module, and pre-process the learning behavior data. The learning behavior data includes knowledge point learning data, test data, and review data; the knowledge point learning data includes login frequency, online learning time, video viewing records, number of visits, and visit duration; test data includes test scores, test accuracy, error types, and test time; review data includes review frequency, review content, and review effect. Pre-process the learning behavior data, including extracting data features, processing missing values ​​and outliers, and performing data standardization.

[0057] For the quantitative evaluation of learning and memory effects in the learning outcome feedback module, we can design a new formula that will comprehensively consider multiple factors such as the student's answering speed, accuracy, and review frequency. Through this formula, we can get a quantitative learning and memory effect score, thereby more accurately evaluating the student's learning status.

[0058] For the quantitative evaluation of learning and memory effects, the formula is as follows:

[0059] Learning and memory effect score = (correct rate × answering speed coefficient × review frequency coefficient) / total test time;

[0060] Among them: the correct rate is the correct rate of students' answers, that is, the ratio of the number of correctly answered questions to the total number of questions, which reflects the students' mastery of the knowledge points; correct rate = (number of correctly answered questions / total number of questions) × 100%;

[0061] Answering speed coefficient: The answering speed coefficient reflects the speed at which students complete questions within the specified time. In order to encourage students to improve their answering speed while ensuring the accuracy, a speed threshold is set according to the answering situation. If the threshold is exceeded, the speed coefficient bonus will be obtained.

[0062] For example, if the speed threshold is set to 1 minute per question, if the average time for students to answer a question is less than or equal to 1 minute, the speed coefficient is 1.0; if the average time for each question is greater than 1 minute but less than or equal to 1.5 minutes, the speed coefficient is 0.9; and so on, set different speed ranges and corresponding coefficients.

[0063] Review frequency coefficient: The review frequency coefficient reflects the review of the content that students have learned. Multiple reviews help deepen memory and improve learning results. Set different coefficients based on the number of times students review each knowledge point.

[0064] For example, set the coefficient for reviewing once to 1.0, the coefficient for reviewing twice to 1.1, the coefficient for reviewing three times to 1.2, and so on, to encourage multiple reviews.

[0065] The total test time is the total time it takes for students to complete all questions, in hours. This value is used to normalize the learning and memory effect score so that the scores of students with different answering times are comparable.

[0066] Through this formula, a quantitative learning and memory effect score can be obtained, which takes into account multiple factors such as the student's answering speed, accuracy, and review frequency, and can more accurately evaluate the student's learning status. At the same time, the formula can also be adjusted and optimized according to actual conditions to meet the evaluation needs in different learning scenarios.

[0067] The present invention combines the Ebbinghaus forgetting curve and the mind map for review, so that students can remember the knowledge points more deeply and lastingly, can recall the knowledge learned more quickly in the exam, and can apply the learned content more accurately in clinical practice. In addition, due to the visualization characteristics of the mind map, students can also more intuitively understand the connection and difference between knowledge, which helps to form a systematic knowledge system, which can not only improve students' learning efficiency, but also help them establish a systematic oncology knowledge system, laying a solid foundation for future clinical practice.

[0068] Based on the same inventive concept, the present application also provides a device and a computer-readable storage medium, including:

[0069] A memory storing executable program code;

[0070] a processor coupled to the memory;

[0071] The processor calls the executable program code stored in the memory to execute the tumor auxiliary memory system based on the Ebbinghaus forgetting curve as described above.

[0072] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the described tumor auxiliary memory system based on the Ebbinghaus forgetting curve.

[0073] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0074] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0075] Finally, it should be noted that the contents disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tumor auxiliary memory system based on the Ebbinghaus forgetting curve, characterized in that: include: A knowledge point determination module determines the knowledge points of tumor teaching; the knowledge points include tumor classification, clinical manifestations, and treatment methods; A review module, based on the principle of the Ebbinghaus forgetting curve, combines the information of the knowledge points to determine the review time, and generates a review plan corresponding to the review time; the review time includes the first day, the second day, the fourth day, the seventh day or the fifteenth day; A mind map module generates a tumor-centered mind map based on the knowledge points and the review plan; the mind map includes a number of secondary sub-nodes including the review plan, and each of the secondary sub-nodes is used to refine and classify the knowledge points.

2. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 1, characterized in that: It also includes a cloud, a teacher terminal and a student terminal, wherein the cloud includes a database storing the knowledge points, mind maps and review plans; The student terminal is used to obtain learning behavior information and learning progress, and manage mind maps and review plans; The teacher terminal is used to view the mind map module of the student terminal, obtain the learning behavior data of the student terminal, and adjust the teaching plan according to the learning behavior data.

3. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 2, characterized in that: The cloud includes an API interface connected to an AI auxiliary module. The AI ​​auxiliary module generates recommended teaching courses and learning status reports based on learning behavior data, and interacts with the student terminal or teacher terminal in natural language.

4. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 2, characterized in that: The mind map module performs time planning through a Gantt chart according to the time review interval and the priority of the task point, and dynamically adjusts the task bars and review time nodes in the Gantt chart according to the learning behavior data; the horizontal axis of the Gantt chart is divided into days, weeks, and months, and the vertical axis represents the review task bars, which include knowledge points and review time nodes.

5. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 2, characterized in that: The student terminal also includes a test module, which generates corresponding test questions according to the review plan and the mind map and obtains test data; each of the test questions includes basic questions, extended questions and comprehensive questions of knowledge points.

6. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 5, characterized in that: It also includes a learning outcome feedback module, which establishes a learning scoring system based on the test module and the knowledge point module, and analyzes learning outcomes through a learning outcome feedback model based on the learning behavior data of the student terminal to determine the degree of mastery of knowledge points; The learning behavior data includes knowledge point learning data, test data and review data; The knowledge point learning data includes login frequency, online learning time, video viewing records, number of visits, and visit duration; the test data includes test scores, test accuracy, error types, and test time; and the review data includes review frequency, review content, and review effect.

7. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 6, characterized in that: The learning outcome feedback model is used to quantitatively evaluate the learning and memory effects, and is calculated using the following formula: Learning and memory effect score = (correct rate × answering speed coefficient × review frequency coefficient) / total test time; Among them: the correct rate is the correct rate of students' answers, that is, the ratio of the number of questions answered correctly to the total number of questions; Correct rate = (number of questions answered correctly / total number of questions) × 100%; The answering speed coefficient is used to reflect the speed at which students complete questions within the specified time and is determined based on the answering speed; The review frequency coefficient is used to reflect the students’ review of the learned content and is determined based on the number of reviews; The total test time is the total time it takes students to complete all questions, in hours.

8. The tumor auxiliary memory system based on the Ebbinghaus forgetting curve according to claim 6, characterized in that: The mind map module generates a mind map by combining color marking and Gantt chart; the color marking is related to the mastery degree of knowledge points of the learning outcome feedback module; the color marking includes the color, font and shading of the mind map.

9. A tumor auxiliary memory device based on the Ebbinghaus forgetting curve, characterized in that: include: Memory; A processor and a computer program stored in a memory and executable on the processor, wherein when the processor executes the computer program, the tumor auxiliary memory system based on the Ebbinghaus forgetting curve as described in any one of claims 1 to 8 is implemented.

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