Platform for safety education and training of industrial control system

Abstract

The invention discloses a platform for safety education and training of an industrial control system, and relates to the technical field of education platforms. The platform comprises the following steps: step 1, inputting student information; step 2, basic course learning; step 3, professional course learning; 4, performing practical operation training: performing attack and defense simulation on the trainees and the AI; 5, big data evaluation and analysis: performing learning habit analysis, strong and weak item analysis, micro-expression analysis, character analysis and learning optimization analysis on students; performing course management analysis and teaching optimization analysis on the teacher; step 6, comprehensive learning: courses needing to be remedied by the trainees are analyzed, the courses are sorted according to score proportions and learning strength conditions, and corresponding teaching schemes are produced; in the learning process, comprehensive testing is carried out on trainees; and after the trainees complete the comprehensive training, performing an evaluation test, and analyzing the grades of the trainees according to the test scores of the trainees. The pertinence and practicability of safety education and training of the industrial control system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of education platforms, and in particular to a platform for industrial control system safety education and training. Background Art

[0002] With the widespread application of industrial control systems, cyber attacks are increasing, posing a serious threat to enterprise production safety. Improving employees' safety awareness and skills is the key to ensuring industrial control security.

[0003] Existing industrial control system security education and training is mainly conducted in the form of offline lectures. This teaching method often requires high costs for students, long teaching time, and consumes a lot of manpower and time costs.

[0004] Therefore, integrating online teaching platforms with industrial control system safety education and training is crucial to improving existing safety education and training content. Existing training content is often disconnected from the actual operating environment and requirements of industrial control systems, lacking relevance and practicality. Training content can be overly theoretical and difficult to directly apply to real-world work scenarios, resulting in poor training effectiveness.

[0005] Furthermore, industrial control system technologies and security threats are constantly evolving, but existing security education and training content is often not updated quickly enough to reflect the latest security threats and protection technologies. This prevents trainees from acquiring the latest security knowledge and skills, increasing the risk of system attacks.

[0006] Therefore, the present invention provides a platform for industrial control system safety education and training. Through professional course learning and practical course design, it increases the pertinence and practicality of the safety education and training process. At the same time, it uses the self-learning ability of AI to increase the update speed of the safety education and training platform. Summary of the Invention

[0007] In order to solve the problem that safety education and training in the existing technology lack pertinence and practicality, the present invention provides a platform for industrial control system safety education and training, which increases the pertinence and practicality of the safety education and training process through the design of professional course learning and practical courses.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: A platform for industrial control system security education and training, comprising the following steps:

[0009] Step 1: Enter student information: Set up a student account and password, enter the student's basic information, and create a student learning account;

[0010] Step 2, Basic Course Learning: The basic course teacher conducts online teaching on basic knowledge and general knowledge for the students, conducts online Q&A, and conducts basic unit tests on the students during the learning process. After the students complete the basic course learning, a basic course exam is conducted;

[0011] Step 3, Professional Course Learning: When the students pass the basic course exam, the platform identifies the relevant majors of the students based on their basic information, matches relevant professional course teachers to conduct online professional knowledge teaching for the students, conducts online Q&A, and conducts professional unit tests on the students during the learning process. After the students complete the professional course learning, a professional course exam is conducted;

[0012] Step 4, Practical Training: When the students pass the professional course exam, practical training is conducted for the students, allowing the students to conduct attack and defense simulations with AI. After the students complete the practical training, a practical exam is conducted. After the students complete the practical exam, AI scores and evaluates the students' practical training based on their attack and defense operations;

[0013] Step 5, Big Data Evaluation and Analysis: Based on the students' learning situations, analyze the students' learning habits, strengths and weaknesses, micro-expressions, personalities, and learning optimization; based on the teachers' teaching situations, conduct course management analysis and teaching optimization analysis for the teachers;

[0014] The platform divides each course into different color blocks according to the students' course learning situations, and connects different color blocks with different color connecting lines to form an interactive knowledge graph;

[0015] When a student finishes learning a certain course, the course is lit up by a color other than gray and red;

[0016] When a student finishes learning a certain course and this course has the same key knowledge points as the previously learned courses, the platform connects the courses with the same key knowledge points and lights them up together with the same color; if a certain course contains multiple knowledge points, the platform connects the courses with the same knowledge points with different color connecting lines;

[0017] When a student finishes learning the courses with the same key knowledge, the courses with the same key knowledge will be connected into a straight line of the same color;

[0018] When a student finishes learning all the interrelated courses, all the interrelated courses will be connected according to the same knowledge points with different color connecting lines to form a knowledge connection network, and the platform displays the specific knowledge points represented by each color block and each color connection block;

[0019] When a student skips a certain course or part of a course during the learning process, the platform marks the unlearned part of the course in gray, and the courses that have been completed but have no direct connection with the previous ones are marked in red. When the student completes the make-up study of the skipped courses, the courses marked in red will be connected to the make-up courses and become the same color;

[0020] Big data analyzes and displays the percentage of learning completion progress according to the connection status and color change status.

[0021] Step 6, comprehensive learning: When the student passes the practical exam, comprehensive training is carried out for the student. During the learning process, big data analyzes the courses that the student needs to make up according to the student's learning situation, sorts the courses according to the score percentage and learning strength, and the courses with low scores and weak learning situations are arranged in the front, and the corresponding teaching plan is produced; during the learning process, comprehensive tests are carried out on the student; after the student completes the comprehensive training, an assessment exam is carried out, and according to the student's exam results, the student's level is analyzed.

[0022] Furthermore, in Step 1, the basic information of the student includes name, gender, age, education level and major.

[0023] Furthermore, in Step 2, a basic knowledge course video for the student's teacher to review and study will be generated after each class.

[0024] Furthermore, in Step 2, there is no limit to the number of times for the basic unit test, and there are three chances for the basic course exam.

[0025] Furthermore, in Step 3, the platform provides relevant professional knowledge videos and relevant professional attack and defense cases according to the student's major.

[0026] Furthermore, in Step 3, there is no limit to the number of times for the professional unit test, and there are three chances for the professional course exam.

[0027] Furthermore, in Step 4, the practical training includes attack simulation training and defense simulation training. The student chooses to be the attacker or the defender. When the student succeeds in the attack and defense, the AI conducts operation learning. When the student fails in the attack and defense continuously twice, the AI reduces the training difficulty, and there are three chances for the practical exam.

[0028] Furthermore, in Step 5, the learning habit analysis includes the daily learning duration, learning progress, question-solving time, number of questions brushed and question accuracy rate;

[0029] The strength and weakness analysis includes the knowledge types that the student is good at or prone to make mistakes and the question types that the student is good at or prone to make mistakes;

[0030] The learning optimization analysis includes suggestions for adjusting the learning duration, suggestions for adjusting the learning progress and suggestions for adjusting the question-solving time.

[0031] Furthermore, in step five, course management refers to teachers adding and deleting courses, and course management analysis includes course addition suggestions and course deletion suggestions.

[0032] Furthermore, in step six, there is no limit on the number of times you can take the comprehensive test, but you can only take the assessment test once.

[0033] The basic program offers the following benefits: 1. By entering students' basic information and identifying their relevant majors, the platform automatically matches them with relevant professional courses and provides case studies and videos, enabling personalized learning paths and improving learning efficiency. After students complete basic, specialized, and comprehensive courses, big data analyzes their progress and identifies key courses they need to review. Courses are then ranked by score percentage and learning strengths, and corresponding teaching plans are generated, further increasing the relevance of the learning process and improving student learning efficiency.

[0034] 2. Big data analyzes students' personalities based on their language, actions, and facial expressions during class. Combined with their age, this is used to determine the appropriate teaching method for them. For example, if a student is cheerful and lively and is 18 years old, a game-based teaching method can be used, comparing the attack and defense process to a game. This teaching plan can then be recommended to the teacher for optimization.

[0035] Big data can monitor students' strengths and weaknesses based on their language, actions, and facial expressions when doing questions. For example, if a student frowns and takes a long time to do a question, it can be judged that the student performs poorly on this type of question and needs to strengthen his or her learning of this type of knowledge. Combined with the student's scores, a more accurate judgment can be made, and ultimately corresponding learning optimization suggestions can be provided to the student. At the same time, the teaching teacher can be informed of the student's strengths and weaknesses, so that the teaching teacher can adjust the teaching content. Big data will collect the students' learning process and provide feedback on their learning situation. Students can understand their learning progress and learning situation in real time, check their test scores, exam scores, wrong question collections, completed courses and unfinished courses, so that students can understand their learning situation in a digitized way and adjust their learning methods.

[0036] The big data evaluation and analysis function can conduct in-depth analysis of students' learning habits and performance from multiple dimensions, provide teachers with teaching optimization suggestions, and provide students with personalized learning suggestions, helping teachers and students grow together.

[0037] 3. The platform will form an interactive knowledge graph based on the course learning situation of the trainees; using color-coded blocks and connecting lines, the platform can intuitively display the learning progress and knowledge mastery of the trainees. Trainees can specifically understand the completion status of each of their courses in a charted manner based on the display of the colored blocks and the connection of the connecting lines in the graph.

[0038] When trainees skip certain courses, the platform uses gray and red markings to remind trainees to pay attention to the absence of these courses, and reconnects the courses after remediation to form a complete knowledge chain. This personalized guidance helps trainees learn targeted according to their actual situations. The learning progress percentage analysis provided by the platform enables trainees to clearly understand their learning effectiveness, adjust their learning strategies in a timely manner, and improve learning efficiency and results.

[0039] At the same time, the platform shows the specific knowledge points represented by each color block and each color connection block. Trainees can select courses for learning according to the classification of knowledge points based on their learning situations, so as to achieve targeted learning, which is convenient for trainees to strengthen and consolidate their weak links.

[0040] 4. Compared with the single teaching form in the prior art, the design of the attack and defense simulation in the practical training enables trainees to perform real attack and defense operations in a simulated environment, improving the trainees' actual combat ability and preparing them for future work. At the same time, the AI can not only score and evaluate the trainees' attack and defense operations, but also automatically reduce the training difficulty when the trainees' attacks and defenses fail continuously to adjust the training rhythm, help the trainees improve gradually. And during the process of training the trainees, the AI is also constantly learning the operation methods of different trainees, improving its own operations, and then can increase the training difficulty, better train with the trainees, and at the same time improve its own attack and defense performance to better protect the platform security.

[0041] 5. From basic courses to professional courses, and then to practical training and comprehensive learning, corresponding tests and examinations are set at each stage to ensure that trainees can receive sufficient learning and evaluation at each stage and guarantee the learning quality. And the learning mechanism is flexible. There is no limit to the number of times for the basic unit tests and comprehensive tests. There are limits to the number of times for the basic course examinations, professional course examinations, and practical examinations, but multiple opportunities are provided. This design not only ensures that trainees have sufficient learning time, but also ensures that trainees can complete the learning tasks within a limited time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of the platform for industrial control system safety education and training in an embodiment of the present invention.

[0043] Figure 2 It is a flowchart of the basic course in an embodiment of the present invention.

[0044] Figure 3 This is the flowchart of the professional courses in the embodiments of the present invention.

[0045] Figure 4 This is the flowchart of the attack and defense model training in the embodiments of the present invention.

[0046] Figure 5 This is the flowchart of the big data evaluation and analysis in the embodiments of the present invention.

[0047] Figure 6 This is the schematic diagram of the interactive knowledge graph in the embodiments of the present invention. Specific implementation manners

[0048] The following is a further detailed description through specific implementation manners:

[0049] The embodiments are basically as shown in the appendix Figures 1-6 as follows:

[0050] A platform for industrial control system safety education and training includes the following steps:

[0051] Step 1, student information entry: The student sets an account password and enters information such as his / her name, gender, age, education level, and major to form his / her own learning account. Each time when learning, the account password needs to be used for login to ensure the integrity and security of the learning process.

[0052] Step 2, basic course learning: The basic course teacher conducts online teaching of basic knowledge and general knowledge to the students. During the teaching process, online Q&A is carried out. After each class, a basic knowledge course video for the students and the teacher to review and learn is generated, which is convenient for the students to review relevant knowledge and also convenient for the teacher to optimize the teaching plan.

[0053] During the learning process, the basic course teacher conducts basic unit tests on the students to understand the students' learning situation in real time. The students can also conduct self-tests to understand their own learning situation and improve their learning efficiency.

[0054] After the students complete the basic course learning, they take the basic course exam. There is no limit on the number of times for the basic unit test, and there are three chances for the basic course exam. When the students pass the basic course exam, they can proceed to the professional course learning. If they fail three times, they need to restart the basic course learning.

[0055] Step 3, professional course learning: When the students pass the basic course exam, the platform identifies the relevant majors of the students according to their basic information, provides relevant professional knowledge videos and relevant professional attack and defense cases for the students to learn, and matches relevant professional course teachers to conduct online professional knowledge teaching for the students. During the teaching process, online Q&A is carried out.

[0056] During the learning process, the professional course teacher conducts professional unit tests on the students to understand their learning situation in real time. The students can also conduct self-tests to further improve their professional unit knowledge.

[0057] After the students complete the professional course learning, they take the professional course exam. There is no limit to the number of professional unit tests, but there are three chances for the professional course exam. When the students pass the professional course exam, they can proceed with practical training. If they fail three times, they need to restart the professional course learning.

[0058] Step 4, Practical training: When the students pass the professional course exam, they are given practical training, which includes attack simulation training and defense simulation training. The students can choose to be the attacker or the defender. When the students succeed in the attack and defense, the AI records the operation learning in real time and updates the attack and defense database in real time to keep the database updated and facilitate the improvement of the training difficulty during training. When the students fail in the attack and defense twice in a row, the AI reduces the training difficulty and adjusts the training rhythm to help the students gradually improve.

[0059] After each practical training session, the AI scores the students' attack and defense operations. The full score is 100 points, with 60 points for the attack and defense operations and 40 points for the attack and defense results. 20 points are deducted for a failed attack and defense, and 40 points are deducted for consecutive failures. There is no deduction for a successful attack and defense.

[0060] There are three chances for the practical exam. If the students fail three times, they need to restart the professional course learning. When the students pass the practical exam, they can proceed with comprehensive learning.

[0061] The design of the practical training enables the students to perform real attack and defense operations in a simulated environment, improving their actual combat ability and preparing them for future work. At the same time, the AI can not only score and evaluate the students' attack and defense operations, but also automatically reduce the training difficulty when the students fail in the attack and defense continuously to adjust the training rhythm and help the students gradually improve. Moreover, during the process of training the students, the AI is also constantly learning the operation methods of different students to improve its own operations, thereby increasing the training difficulty, better training with the students, and at the same time enhancing its own attack and defense performance to better protect the platform security.

[0062] Step 5, Big data evaluation and analysis: Based on the students' learning situation, conduct learning habit analysis, strength and weakness analysis, micro-expression analysis, personality analysis, and learning optimization analysis on the students; based on the teachers' teaching situation, conduct course management analysis and teaching optimization analysis on the teachers;

[0063] The platform divides each course into different color blocks according to the students' course learning situation, and connects the different color blocks with connecting lines of different colors to form an interactive knowledge graph;

[0064] After a student finishes learning a certain course, the course is lit up in a color other than gray and red;

[0065] When a student finishes learning a certain course and this course has the same key knowledge points as the previously learned courses, the platform connects the courses with the same key knowledge points and lights them up together in the same color; if a certain course contains multiple knowledge points, the platform connects the courses with the same knowledge points according to connecting lines of different colors;

[0066] For example: After a student finishes learning the first section of Course A, the first section of Course A is lit up in blue.

[0067] When a student finishes learning the second section of Course A, the platform recognizes that the second section of Course A has the same key knowledge points as the first section of Course A. The second section of Course A will be connected to the first section of Course A and lit up together in blue, and the platform will display the key knowledge points represented by the blue color block.

[0068] When a student finishes learning the first section of Course B, the platform recognizes that the first section of Course B and the first section of Course A have the same knowledge points. The first section of Course B and the first section of Course A are connected by a green connecting line, and the platform will display the knowledge points represented by the green connecting line.

[0069] After a student finishes learning courses with the same key knowledge, the courses with the same key knowledge will be connected into a straight line of the same color;

[0070] After a student finishes learning all the interrelated courses, all the interrelated courses will be connected according to the same knowledge points by connecting lines of different colors to form a knowledge connection network, and the platform will display the specific knowledge points represented by each color block and each color connection block;

[0071] When a student skips a certain course or part of a course during the learning process, the platform marks the unlearned part of the course as gray, and the courses that have been completed but lack a direct connection with the previous ones are marked as red. After the student completes the tutoring of the skipped course, the courses marked as red will be connected to the tutored course and change to the same color;

[0072] For example: After a student finishes learning the first section of Course A, the first section of Course A is lit up in blue. The student skips the second section of Course A and learns the third section of Course A. The platform recognizes that the key knowledge in the third section of Course A has never been mentioned and lacks relevance during the student's learning process, so it will mark the third section of Course A as red, and recognizes that this key knowledge is mentioned in the second section of Course A, so it will mark the skipped second section of Course A as gray to remind the student to learn this course. After the student finishes learning the second section of Course A, the first section, the second section, and the third section of Course A will turn blue and be connected by a blue connecting line.

[0073] Big data analyzes and displays the percentage of learning completion according to the connection status and color change status.

[0074] In the design of the interactive knowledge graph, using color blocks and connecting lines of different colors, the platform can intuitively display the learning progress and knowledge mastery of students. Students can specifically understand the completion status of each of their courses in a charted manner based on the display status of the color blocks and the connection status of the connecting lines in the knowledge graph. And this visual presentation method enables students to clearly see the courses they have completed, the courses they are currently learning, and the courses they have not yet learned, thus enhancing the motivation and sense of purpose in learning.

[0075] The platform designs an association network by establishing the relevance between various knowledge points, thereby helping students establish connections between knowledge and promoting the integration and systematization of knowledge. The construction of the interactive knowledge graph helps students better understand the internal connections between various courses and form a complete knowledge system. The platform dynamically adjusts the display of the knowledge graph according to the learning situation of students, providing personalized learning path guidance for students. When students skip certain courses, the platform uses gray and red marks to remind students to pay attention to the absence of these courses and reconnect the courses after remediation to form a complete knowledge chain. This personalized guidance helps students learn targeted according to their actual situation. Through the guidance of the interactive knowledge graph, students can more quickly find the connections and differences between knowledge, avoid repeated learning and missing important knowledge points. At the same time, the analysis of the learning progress percentage provided by the platform enables students to clearly understand their learning achievements, adjust their learning strategies in a timely manner, and improve learning efficiency and effectiveness.

[0076] The color-coded display of learning progress and the construction of the interactive knowledge graph make the learning process more vivid and interesting, enhancing the learning experience and participation of students. In the process of exploring the knowledge graph, students can not only deepen their understanding and memory of knowledge but also feel the joy and sense of achievement in learning.

[0077] At the same time, the platform displays the specific knowledge points represented by each color block and each color connection block. Students can select courses for learning according to the classification of knowledge points based on their learning situation, so as to achieve targeted learning and facilitate students to strengthen and consolidate their weak links.

[0078] Big data analyzes the personality of students based on their language, actions, expressions, and gestures during class, and combines the age of the students to judge the suitable teaching methods for them. For example, if a student is cheerful and lively and 18 years old, the game teaching method can be used, comparing the offensive and defensive processes to game battles for teaching, and recommending this teaching plan to the teaching teacher for optimizing the teaching plan.

[0079] Based on the language, actions, expressions, and demeanor of students when doing exercises, big data monitors the students' strong and weak knowledge areas. For example, if a student frowns and takes a long time to do an exercise, it is judged that the student is not good at this type of question and needs to strengthen the study of this type of knowledge. Combining with the students' scores on the exercises, more accurate judgments are made. Finally, corresponding learning optimization suggestions are provided for the students, and the teacher is informed of the student's strengths and weaknesses to facilitate the teacher's adjustment of teaching content. Big data will collect the students' learning process information and provide feedback on the students' learning situation. Students can understand their learning progress and situation in real time, view their test scores, exam scores, wrong question sets, completed courses, and uncompleted courses, enabling students to understand their learning situation in a data-driven manner and adjust their learning methods.

[0080] The big data evaluation and analysis function can deeply analyze the learning habits and grades of students from multiple dimensions, provide teaching optimization suggestions for teachers, and provide personalized learning suggestions for students, helping teachers and students grow together.

[0081] Step 6: Comprehensive learning: After the student passes the practical exam, comprehensive training is carried out for the student. During the learning process, big data analyzes the courses that the student needs to make up according to the student's learning situation, sorts the courses according to the score ratio and learning strength, with the courses with lower scores ranked higher and marked in red to strongly remind the student to study, and generates the corresponding course arrangement list and teaching plan; during the learning process, comprehensive tests are carried out on the student.

[0082] After the student completes the comprehensive training, an evaluation exam is conducted. Based on the student's exam scores, the student's grade is analyzed. A comprehensive score of 80 - 100 is considered excellent, 60 - 80 is considered qualified, and below 60 is considered unqualified. Unqualified students need to start learning from the basic knowledge courses again.

[0083] In this way, from basic courses to professional courses, then to practical training and comprehensive learning, corresponding tests and exams are set at each stage to ensure that students can receive sufficient learning and evaluation at each stage and guarantee the learning quality. Moreover, the learning mechanism is flexible. There is no limit to the number of times for basic unit tests and comprehensive tests. There are limits to the number of times for basic course exams, professional course exams, and practical exams, but multiple opportunities are provided. This design not only ensures that students have sufficient learning time but also ensures that students can complete the learning tasks within a limited time.

[0084] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0085] The above are only embodiments of the present invention. Common knowledge such as specific structures and characteristics in the prior art is not described in detail herein. Those of ordinary skill in the art know all the common general technical knowledge in the technical field to which the invention pertains before the filing date or the priority date, can know all the prior art in this field, and have the ability to apply conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, complete and implement this solution in combination with their own abilities. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement this application. It should be pointed out that, for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. A platform for industrial control system safety education and training, characterized in that: It includes the following steps: Step 1, student information entry: Set the student account password, enter the basic information of the student, and form a student learning account; Step 2, basic course learning: The basic course teacher conducts online teaching of basic knowledge and general knowledge to the students, conducts online Q&A, and during the learning process, conducts basic unit tests on the students. After the students complete the basic course learning, a basic course exam is conducted; Step 3, professional course learning: When the students pass the basic course exam, the platform identifies the relevant majors of the students based on their basic information, matches relevant professional course teachers to conduct online professional knowledge teaching to the students, conducts online Q&A, and during the learning process, conducts professional unit tests on the students. After the students complete the professional course learning, a professional course exam is conducted; Step 4, practical training: When the students pass the professional course exam, conduct practical training on the students, let the students conduct offensive and defensive simulations with the AI. After the students complete the practical training, a practical exam is conducted. When the students complete the practical exam, the AI scores and evaluates the students' practical training based on their offensive and defensive operations; Step 5, big data evaluation and analysis: Analyze the students' learning habits, strengths and weaknesses, micro-expressions, personalities, and learning optimization based on the students' learning situations; Analyze the course management and teaching optimization of the teachers based on the teachers' teaching situations; The platform divides each course into different color blocks according to the students' course learning situations, and connects the different color blocks with different color connecting lines to form an interactive knowledge graph; When a student finishes learning a certain course, the course is lit up with a color other than gray and red; When a student finishes learning a certain course and this course has the same key knowledge points as the previously learned courses, the platform connects the courses with the same key knowledge points and lights them up in the same color; If a certain course contains multiple knowledge points, the platform connects the courses with the same knowledge points with different color connecting lines; When a student finishes learning the courses with the same key knowledge, the courses with the same key knowledge will be connected into a straight line of the same color; When a student finishes learning all the interrelated courses, all the interrelated courses will be connected according to the same knowledge points with different color connecting lines to form a knowledge connection network, and the platform displays the specific knowledge points represented by each color block and each color connection block; When a student skips a certain course or a part of the course during the learning process, the platform marks the unlearned part of the course as gray, and the courses that are completed but lack a direct connection with the previous ones are marked as red. When the student completes the makeup of the skipped course, the courses marked as red will be connected to the makeup course and become the same color; Big data analyzes and displays the percentage of the learning completion progress according to the connection situation and color change situation; Step 6, Comprehensive Learning: After the trainee passes the practical operation exam, comprehensive training is carried out for the trainee. During the learning process, big data analyzes the courses that the trainee needs to make up according to the trainee's learning situation, sorts the courses according to the score ratio and the strength and weakness of learning, with the courses with low scores and weak learning situations ranked first, and corresponding teaching plans are produced; during the learning process, comprehensive tests are carried out on the trainee; after the trainee completes the comprehensive training, an assessment exam is conducted, and the trainee's level is analyzed based on the trainee's exam results.

2. The platform for industrial control system safety education and training according to claim 1, characterized in that: In Step 1, the basic information of the trainee includes name, gender, age, education level, and major.

3. The platform for industrial control system safety education and training according to claim 2, characterized in that: In Step 2, a basic knowledge course video for the trainee's teacher to review and study is generated after each class.

4. The platform for industrial control system safety education and training according to claim 3, wherein: In Step 2, there is no limit to the number of times for the basic unit test, and there are three chances for the basic course exam.

5. The platform for industrial control system safety education and training according to claim 4, characterized in that: In Step 3, the platform provides relevant professional knowledge videos and relevant professional attack and defense cases according to the trainee's major.

6. The platform for industrial control system safety education and training according to claim 5, wherein: In Step 3, there is no limit to the number of times for the professional unit test, and there are three chances for the professional course exam.

7. The platform for industrial control system safety education and training according to claim 6, characterized in that: In Step 4, the practical training includes attack simulation training and defense simulation training. The trainee chooses to be the attacker or the defender. When the trainee succeeds in attack and defense, the AI conducts operation learning. When the trainee fails in attack and defense twice in a row, the AI reduces the training difficulty. There are three chances for the practical operation exam.

8. The platform for industrial control system safety education and training according to claim 7, characterized in that: In Step 5, the learning habit analysis includes daily learning duration, learning progress, question-solving time, number of questions brushed, and question accuracy rate; The strength and weakness analysis includes the types of knowledge that the trainee is good at or prone to mistakes and the types of questions that the trainee is good at or prone to mistakes; The learning optimization analysis includes suggestions for adjusting the learning duration, suggestions for adjusting the learning progress, and suggestions for adjusting the question-solving time.

9. The platform for industrial control system safety education and training according to claim 8, characterized in that: In Step 5, course management refers to the teacher adding and deleting courses. The course management analysis includes suggestions for adding courses and suggestions for deleting courses. The teaching optimization analysis includes suggestions for adjusting the teaching duration, suggestions for adjusting the teaching plan, and suggestions for adjusting the teaching content.

10. The platform for industrial control system safety education and training according to claim 9, wherein: In Step 6, there is no limit to the number of times for the comprehensive test, and there is one chance for the assessment exam.

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