Power grid equipment technology supervision knowledge teaching system and use method

Abstract

The invention provides a power grid equipment technical supervision knowledge teaching system and a use method thereof. The system comprises five modules: a data storage module, which stores standard files, equipment pictures and other technical supervision knowledge according to equipment classification and can be dynamically updated; the man-machine interaction module is used for students to select learning contents and input feedback; the evaluation judgment module pushes questions matched with the difficulty and judges the mastering condition of the trainee; the learning guide module pushes associated learning resources according to the learning selection content and the evaluation result; and the data statistical analysis module generates a learning report and has an early warning function. When the system is used, a student selects a student after login verification, and the system pushes learning knowledge and associated resources. And after learning is completed, evaluation is performed, and learning data is counted and analyzed. The system can realize systematic teaching and effect tracking of technical supervision knowledge, and helps technicians to efficiently improve the technical supervision knowledge level and practical ability.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment knowledge teaching technology, specifically to a power grid equipment technical supervision knowledge teaching system and its usage method. Background Technology

[0002] Technical supervision of power grid equipment is a crucial link in ensuring the safe and stable operation of the power grid, spanning numerous processes including equipment design, manufacturing, installation, acceptance, and operation. Conducting technical supervision can promptly identify and eliminate potential problems, effectively prevent equipment failures, and thus ensure the reliability and stability of the entire power system. With the rapid advancement of new power system construction, the scale of the power grid is constantly expanding, and the number and complexity of power grid equipment are continuously increasing, placing higher demands on the professional capabilities of technical supervision personnel. Technical personnel not only need to master the technical characteristics of various equipment but also need to be familiar with the constantly updated technical supervision standards and specifications.

[0003] However, the current teaching and training system for power grid equipment technical supervision knowledge has significant shortcomings, and a targeted teaching system has not yet been formed, resulting in numerous problems for technical personnel in the learning process: First, knowledge acquisition is fragmented and lacks systematicity. Currently, technical personnel mainly acquire knowledge through self-study of technical supervision regulations, national standards, and enterprise standards. However, these materials are scattered across different documents, lacking hierarchical organization and logical connections. Technical personnel struggle to build a systematic knowledge framework, resulting in a one-sided understanding of technical supervision knowledge and hindering the effective integration of theoretical knowledge with practical work.

[0004] Secondly, case resources are scattered, leading to weak problem-solving capabilities. Studying technical supervision cases is crucial for improving technical supervision levels and capabilities. However, current technical supervision cases are mostly fragmented supervision reports or work summaries, not categorized by equipment type, problem severity, or other factors. Technical personnel struggle to develop a systematic approach to problem-solving from these scattered cases, easily finding themselves adrift and without a clear strategy when facing new challenges.

[0005] Third, there is a lack of tracking of learning outcomes and a lack of feedback on skill improvement. Existing learning models lack effective evaluation and feedback mechanisms. There are no specific assessments for different knowledge modules, nor is there a systematic analysis of learning progress and weaknesses. This results in technical personnel being unable to promptly verify their knowledge mastery after self-study, struggling to identify their own knowledge gaps, lacking targeted learning directions, and experiencing overall low learning efficiency.

[0006] Currently, existing methods for learning technical supervision knowledge are significantly inadequate and fail to meet the skill requirements of power grid equipment technical supervision personnel arising from the rapid development of new power systems. Therefore, there is an urgent need for a specialized teaching system and methodology for power grid equipment technical supervision knowledge. This system should provide rich learning resources, systematic knowledge integration, targeted case studies, and scientific assessment feedback to help technical personnel efficiently improve their technical supervision knowledge and practical skills, ensuring the high-quality implementation of power grid equipment technical supervision work. Summary of the Invention

[0007] To achieve the above objectives, this invention proposes a teaching system and method for technical supervision knowledge of power grid equipment, the specific technical solution of which is as follows: A teaching system for technical supervision of power grid equipment mainly includes 5 modules: (1) Data storage module: Store the corresponding technical supervision knowledge content according to the classification of various power grid equipment, including relevant standard documents, pictures of normal power grid equipment, pictures of power grid equipment with problems, typical cases of power grid equipment problems, and the classification of various problems; (2) Human-computer interaction module: It has an information display unit and an information input unit. The information display unit is used to display the knowledge content of power grid equipment technical supervision, assessment questions, learning progress and learning report. The information input unit is used to input selection instructions, feedback information and assessment answers; (3) Assessment and judgment module: It is used to accurately assess the learning effect of students, including question generation unit and answer judgment unit. Based on the content learned by students, it retrieves the corresponding assessment questions from the data storage module and pushes them to students. It receives the assessment answers submitted by students and compares the assessment answers with the preset standard answers to determine whether students have mastered the content. (4) Learning guidance module: It is used to make judgments based on the learning content selected by the student through the human-computer interaction module and the learning effect obtained by the assessment module, retrieve related learning resources from the data storage module and push them to the human-computer interaction module for the student to learn; (5) Statistical Analysis Module: Used to collect data on students’ learning time, types of learning devices, types of learning knowledge content and assessment scores, and to analyze the statistical data to generate individual learning analysis reports and overall teaching analysis reports.

[0008] The power grid equipment includes transformers, wall bushings, reactors, capacitors, arc suppression coils, combined electrical appliances, circuit breakers, disconnect switches, switch cabinets, current transformers, voltage transformers, surge arresters, grounding grids, fire protection equipment and facilities, transmission towers, conductors and ground wires, fittings, insulators, lightning rods, line foundations, transmission lightning protection grounding, cables, transmission external insulation, and station external insulation.

[0009] The relevant standard documents include technical supervision implementation rules, national standards, enterprise standards, industry standards, and group standards.

[0010] The data storage module also has a knowledge update function, which can periodically retrieve the latest power grid equipment standard documents, problem cases and photos from external databases to update the stored knowledge content.

[0011] The information input unit of the human-computer interaction module supports keyboard input, touch screen input, and voice input.

[0012] The question generation unit of the assessment module automatically adjusts the difficulty, question type, and number of assessment questions based on the difficulty level of the knowledge content learned by the students.

[0013] The statistical analysis module also has an early warning function. When the analysis shows that a student's assessment score is lower than the preset passing score multiple times in a row, an early warning message is sent to the student and teaching management personnel.

[0014] A method for using a power grid equipment technical supervision knowledge teaching system includes the following steps: S1: Login to the system. Students log in to the system through the human-computer interaction module. The human-computer interaction module verifies the student's identity information. After successful verification, the student enters the learning interface. S2: Learning Selection. In the learning interface, students select the type of power grid equipment to be learned and the corresponding knowledge content through the human-computer interaction module. The human-computer interaction module sends the selection command to the data storage module, which retrieves the corresponding knowledge content and displays it to the students through the human-computer interaction module. S3: Learning process. Students learn the knowledge content displayed. During the learning process, they can input feedback information through the human-computer interaction module. The learning guidance module retrieves relevant learning resources from the data storage module based on the feedback information and learning progress, and pushes them to the human-computer interaction module for students to learn. S4: Assessment and Judgment. After completing the knowledge learning, the student initiates an assessment request through the human-computer interaction module. The assessment and judgment module retrieves the assessment questions corresponding to the learning content from the data storage module and pushes them. After the student submits the assessment answers, the assessment and judgment module compares the assessment answers with the standard answers, scores the assessment answers, and feeds back the score results to the human-computer interaction module. S5: Data feedback. The statistical analysis module collects the learning data of students in steps S2-S4, analyzes it, and generates a personal learning analysis report, which is displayed to students through the human-computer interaction module. At the same time, an overall teaching analysis report is generated for teaching management personnel to view.

[0015] Furthermore, in step S1, the human-computer interaction module verifies the student's identity information through methods including account password verification, fingerprint verification, or facial recognition verification.

[0016] Furthermore, in step S4, if the assessment module determines that the student has not mastered the learned content, the learning guidance module will retrieve the key analysis and supplementary learning resources of that part of the knowledge content from the data storage module and push them to the human-computer interaction module for the student to carry out targeted learning.

[0017] Compared with the prior art, the present invention has the following advantages or beneficial effects: First, it helps build a systematic knowledge framework. The teaching system uses a data storage module to store technical supervision knowledge content categorized by power grid equipment, integrating scattered resources such as technical supervision implementation rules and standard documents. The learning guidance module pushes relevant resources based on the learner's selections, helping them build a complete knowledge framework, avoiding fragmented knowledge, and enabling learners to systematically learn and understand technical supervision knowledge.

[0018] Secondly, it effectively enhances problem-solving capabilities. The teaching system systematically categorizes typical technical supervision case resources, covering cases of different problem levels for various types of power grid equipment. Trainees can learn in a targeted manner based on equipment type and problem level. Through systematic case-based teaching, it helps technical personnel develop a systematic approach to problem-solving, effectively improving their ability to handle technical supervision issues.

[0019] Third, it accurately tracks and provides feedback on learning outcomes. The assessment module of this teaching system can generate targeted assessment questions according to the difficulty of the knowledge, and the data statistics and analysis module can collect data such as learning time and assessment scores and generate reports. It can also issue warnings when scores are consistently below standard, allowing students to promptly identify knowledge gaps, clarify their learning direction, and significantly improve the efficiency of mastering technical supervision knowledge. Attached Figure Description

[0020] Figure 1 This is an architecture diagram of the system of the present invention; Figure 2 This is a flowchart illustrating the usage method of the system of the present invention. Detailed Implementation

[0021] The technical solutions of this invention will now be clearly and completely described in conjunction with embodiments thereof. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Example 1: Please refer to Figure 1 , Figure 1 The diagram shown is an architecture diagram of a power grid equipment technical supervision knowledge teaching system, which mainly includes 5 modules: (1) Data storage module: The module stores the corresponding technical supervision knowledge content according to various power grid equipment (such as transformers, wall bushings, combined electrical appliances, circuit breakers, disconnect switches, switchgear, transmission towers, fittings, insulators, etc.), including: ① relevant standard documents (such as technical supervision implementation rules, national standards, enterprise standards, industry standards, group standards); ② pictures of normal power grid equipment (clearly showing the normal appearance, structure and installation status of each component of the power grid equipment); ③ pictures of power grid equipment with problems (presenting different types of problems of power grid equipment, such as galvanized layer peeling, insulator damage, transmission tower corrosion, etc.); ④ typical problem cases of power grid equipment (including problem level, problem description, violation clauses, consequence analysis, and rectification suggestions); ⑤ classification of various problems (including general problems, major problems, and serious problems). At the same time, the data storage module also has a knowledge update function, which can automatically and periodically obtain the latest power grid equipment standard documents, problem cases and photos from external databases, and also supports managers to manually upload and update resources to update the stored knowledge content.

[0023] (2) Human-Computer Interaction Module: This module includes an information display unit and an information input unit. The information display unit shows the technical supervision knowledge content of power grid equipment, assessment questions, learning progress, and learning reports. The information input unit is used to input selection commands, feedback information, and assessment answers. The information input unit provides diverse input methods, including keyboard input (for text input), touchscreen input (for operation selection), and voice input (allowing learners to select learning content or submit feedback via voice commands). Furthermore, the human-computer interaction module also has an identity verification function. When technical personnel log in to the system, they can choose from three methods for identity verification: account password verification, fingerprint verification, and facial recognition verification.

[0024] (3) Assessment and Judgment Module: This module is used to accurately assess students' learning outcomes and includes a question generation unit and an answer judgment unit. The question generation unit retrieves assessment questions corresponding to the learning content selected by the student from the data storage module and pushes them to the student. It can automatically adjust the difficulty, question type, and number of assessment questions based on the difficulty level of the knowledge content learned by the student. The difficulty levels are divided into general, medium, and high difficulty; the question types are multiple choice and true / false; and the number of questions ranges from 10 to 50. The answer judgment unit receives the student's submitted assessment answers, compares them with preset standard answers, and scores them to determine whether the student has mastered the learned content. After the assessment, the assessment and judgment module generates a detailed assessment report, analyzes incorrect answers, and helps students understand and master the learned knowledge.

[0025] (4) Learning Guidance Module: This module is used to make judgments based on the learning content selected by the learner through the human-computer interaction module and the learning effect obtained by the assessment module. It retrieves relevant learning resources from the data storage module and pushes them to the human-computer interaction module for the learner to study. For example, when a learner selects learning content (such as technical supervision knowledge of disconnect switches), if the assessment results show that the learner has not fully mastered a certain knowledge point of the equipment, the learning guidance module will further retrieve the key analysis materials, more cases, and targeted questions for that knowledge point to guide the learner to conduct targeted learning and strengthen their understanding and mastery of that knowledge point.

[0026] (5) Statistical Analysis Module: This module is used for comprehensive statistical analysis and in-depth analysis of students' learning data. The statistical data includes learning time (total learning time, learning time for each type of device), learning device type, learning knowledge content type (standard documents, pictures, cases, etc.), and assessment scores (scores for each assessment, average score, and pass rate). After the analysis is completed, a personal learning analysis report and an overall teaching analysis report are generated. The personal learning analysis report shows the student's learning progress, weak knowledge points, and trends in assessment scores, while the overall teaching analysis report shows statistics on the learning situation of all students. The statistical analysis module also has an early warning function. When the analysis shows that a student's assessment score is lower than the preset passing score (e.g., 60 points) for several consecutive times, an early warning message is sent to the student and teaching management personnel.

[0027] Please refer to Figure 2 , Figure 2 The diagram shows a flowchart illustrating the usage of a power grid equipment technical supervision knowledge teaching system. The method specifically includes the following steps: S1: System Login. Students log in to the system through the human-computer interaction module, selecting their username and password for identity verification on the login interface. The human-computer interaction module verifies the student's identity information. After successful verification, it retrieves the student's personal learning file based on the student's identity information and leads to the learning interface.

[0028] S2: Learning Selection. On the learning interface, students select the type of power grid equipment to learn, "Combined Electrical Appliances," and the corresponding knowledge content, "Detailed Rules for the Implementation of Technical Supervision," through the human-computer interaction module. The human-computer interaction module sends the student's selection command to the data storage module, which retrieves the corresponding knowledge content and displays it to the student through the display unit of the human-computer interaction module.

[0029] S3: During the learning process, students learn the knowledge content displayed in the display unit. During the learning process, they mark the "When closing the circuit breaker, ensure that the circuit breaker is closed in place, and check that the drive crank arm of the telescopic disconnector has passed the 'dead point' and adopt a limit self-locking structure" as the key points of attention through the human-computer interaction module. Based on the feedback information and learning progress, the learning guidance module retrieves multiple normal images of the circuit breaker being closed in place and abnormal images of the circuit breaker not being closed in place from the data storage module and pushes them to the human-computer interaction module for students to learn.

[0030] S4: Assessment and Judgment. After completing the knowledge learning, the student initiates an assessment request through the human-computer interaction module. The assessment and judgment module retrieves the assessment questions (10 multiple-choice questions and 10 true / false questions) corresponding to the learning content from the data storage module and pushes them. After the student submits the assessment answers, the assessment and judgment module compares the assessment answers with the standard answers, scores the assessment answers (the score is 95 points), and feeds back the score results to the human-computer interaction module.

[0031] S5: Data feedback. The statistical analysis module collects the learning data of students in steps S2-S4, analyzes it, and generates a personal learning analysis report (the core content of which is "the mastery rate of knowledge related to the supervision of combined electrical appliances reaches 85%). This report is displayed to students through the human-computer interaction module, and an overall teaching analysis report is generated for teaching management personnel to view.

[0032] Example 2: A knowledge teaching system for power grid equipment technical supervision, the system scheme is the same as in Implementation Example 1.

[0033] Based on the usage method of this teaching system, the specific steps include: S1: System Login. Students log in to the system through the human-computer interaction module. On the login interface, they select facial recognition for identity verification. The human-computer interaction module verifies the student's identity information. After successful verification, it retrieves the student's personal learning file based on the student's identity information and leads to the learning interface.

[0034] S2: Learning Selection. On the learning interface, students select the type of power grid equipment to learn—"transmission towers"—and the corresponding knowledge content—"typical problem cases"—through the human-computer interaction module. The human-computer interaction module sends the student's selection command to the data storage module, which retrieves the corresponding knowledge content and displays it to the student through the display unit of the human-computer interaction module.

[0035] S3: Learning process. Students learn the knowledge content displayed in the unit. During the learning process, they can provide feedback through the human-computer interaction module that "the knowledge related to galvanized layer needs to be strengthened". Based on the feedback information and learning progress, the learning guidance module retrieves multiple technical supervision implementation rules and enterprise standard clauses related to "galvanized layer thickness" and "galvanized layer appearance" from the data storage module and pushes them to the human-computer interaction module for students to learn.

[0036] S4: Assessment and Judgment. After completing the knowledge learning, the student initiates an assessment request through the human-computer interaction module. The assessment and judgment module retrieves the assessment questions (10 multiple-choice questions and 15 true / false questions) corresponding to the learning content from the data storage module and pushes them. After the student submits the assessment answers, the assessment and judgment module compares the assessment answers with the standard answers, scores the assessment answers (score is 88 points), and feeds back the score results to the human-computer interaction module.

[0037] S5: Data feedback. The statistical analysis module collects the learning data of students in steps S2-S4, analyzes it, and generates a personal learning analysis report (the core content of which is "the mastery rate of knowledge related to the supervision of power transmission towers has reached 72%). This report is displayed to the students through the human-computer interaction module, and an overall teaching analysis report is generated for teaching management personnel to view.

[0038] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A grid equipment technology supervision knowledge teaching system, characterized in that, The system comprises five modules: (1) a data storage module: according to various power grid equipment classification, the corresponding technical supervision knowledge content is stored, including relevant standard documents, pictures of normal power grid equipment, pictures of power grid equipment with problems, typical problem cases of power grid equipment, classification of various problems; (2) a man-machine interaction module: with an information display unit and an information input unit, the information display unit is used to display the power grid equipment technical supervision knowledge content, the test questions, the learning progress and the learning report, and the information input unit is used to input the selection instruction, the feedback information and the test answer; (3) an evaluation judgment module: for accurately evaluating the learning effect of the students, including a question generation unit and an answer judgment unit, according to the learned content of the students, the corresponding learning content test questions are retrieved from the data storage module and pushed to the students, the test answers submitted by the students are received, and the test answers are compared with the preset standard answers to judge whether the students master the learned content; (4) a learning guidance module: for judging the learning content selected by the students through the man-machine interaction module and the learning effect obtained by the evaluation judgment module, retrieving the associated learning resources from the data storage module and pushing them to the man-machine interaction module for the students to learn; (5) a statistical analysis module: for statistics of the learning time, the learning equipment types, the learning knowledge content types and the test scores of the students, and analysis of the statistical data to generate personal learning analysis report and overall teaching analysis report.

2. The grid equipment technology supervision knowledge teaching system according to claim 1, characterized in that, The power grid equipment includes transformer, wall bushing, electric reactor, capacitor, arc suppression coil, combined electric appliance, circuit breaker, disconnecting switch, switch cabinet, current transformer, voltage transformer, lightning arrester, grounding net, fire-fighting equipment and facility, power transmission tower, ground wire, hardware, insulator, lightning rod, line foundation, power transmission lightning protection grounding, cable, power transmission external insulation and total station external insulation.

3. The grid equipment technology supervision knowledge teaching system according to claim 1, wherein, The relevant standard documents include technical supervision implementation details, national standards, enterprise standards, industry standards and group standards.

4. The grid equipment technology supervision knowledge teaching system according to claim 1, characterized in that, The data storage module also has a knowledge updating function, which can periodically obtain the latest power grid equipment standard documents, problem cases and photos from external databases to update the stored knowledge content.

5. The grid equipment technology supervision knowledge teaching system according to claim 1, wherein, The information input unit of the man-machine interaction module supports keyboard input, touch screen input and voice input.

6. The grid equipment technology supervision knowledge teaching system according to claim 1, wherein, The question generation unit of the evaluation judgment module automatically adjusts the difficulty, type and quantity of the test questions according to the difficulty level of the knowledge content learned by the students.

7. The grid equipment technology supervision knowledge teaching system of claim 1, wherein, The statistical analysis module also has a warning function, which sends warning information to the students and teaching management personnel when the test scores of the students are continuously lower than the preset passing score line for multiple times.

8. A method of using the grid equipment technical supervision knowledge teaching system according to any one of claims 1-8, characterized in that, The method comprises the following steps: S1: logging in the system, the student logs in the system through the man-machine interaction module, the man-machine interaction module verifies the identity information of the student, and after verification, the student enters the learning interface; S2: learning selection, the student selects the power grid equipment type and the corresponding knowledge content to be learned through the man-machine interaction module in the learning interface, the man-machine interaction module sends the selection instruction to the data storage module, the data storage module retrieves the corresponding knowledge content, and displays it to the student through the man-machine interaction module; S3: learning process, the learner learns the displayed knowledge content, and feedback information can be input through the man-machine interaction module in the learning process, the learning guide module retrieves the associated learning resources from the data storage module according to the feedback information and the learning progress, and pushes them to the man-machine interaction module for the learner to learn; S4: evaluation and judgment, after the learner completes the knowledge learning, initiates the evaluation request through the man-machine interaction module, the evaluation and judgment module retrieves the evaluation questions corresponding to the learning content from the data storage module and pushes them, after the learner submits the evaluation answers, the evaluation and judgment module compares the evaluation answers with the standard answers, scores the evaluation answers, and feeds back the score results to the man-machine interaction module; S5: data feedback, the statistical analysis module analyzes the learning data of the learner in steps S2-S4 to generate a personal learning analysis report, which is displayed to the learner through the man-machine interaction module, and an overall teaching analysis report is generated for the teaching management personnel to view.

9. The method of use of claim 8, wherein, The man-machine interaction module verifies the learner's identity information in the form of account password verification or fingerprint verification or face recognition verification.

10. The method of use of claim 8, wherein, If the evaluation and judgment module judges that the learner does not master the learned content, the learning guide module will retrieve the key analysis and supplementary learning resources of the part of knowledge content from the data storage module and push them to the man-machine interaction module for the learner to learn.

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