CN114141083B - Simulation dynamic learning method and system for transformer substation - Google Patents
Simulation dynamic learning method and system for transformer substation Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 63
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- 238000004519 manufacturing process Methods 0.000 claims abstract description 13
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a transformer substation simulation dynamic learning method and a system, wherein the transformer substation simulation dynamic learning method comprises the steps of constructing a four-level capacity model; manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions; associating the simulation training resources with the four-level capacity model, and inputting the test questions which pass the examination into a final test question library; and responding to the instruction of successful login of the learner, and carrying out dynamic learning in coordination with the operation of the learner. According to the invention, the system firstly builds a substation operation and maintenance personnel capacity four-level model, dynamic learning can be performed on the basis of the four-level capacity model, and the problem of personalized learning of substation staff simulation training is well solved.
Description
Technical Field
The invention belongs to the field of simulation training of power systems, and particularly relates to a substation simulation dynamic learning method and system.
Background
Along with the construction of the intelligent power grid, a large number of new technologies, new systems and new equipment are put into use in a transformer substation, brand new higher requirements are provided for knowledge, technology, skills and the like of power transformation operation and vitamin production operation management personnel, and great challenges are brought to production operation management and talent cultivation of power enterprises. Therefore, training of production operators related to the transformer substation is enhanced, a high-quality staff team with rich expertise and skillful operation skills is cultivated in a short time, safe, stable, economical and efficient operation of the power grid is guaranteed, and development of the intelligent power grid is supported, so that the intelligent power grid is an important and urgent task at present.
Although the traditional substation training simulation system plays an important role in the training of power grid operators, the training content is too dependent on the instructor, and the teaching level of the instructor directly influences the learning effect of the learner; the traditional training mode requires that the instructor is matched with the acceptance degree of most students in the aspect of controlling the training progress, the independent learning requirement of relevant operation personnel of a power grid is difficult to meet regardless of the individual difference of the students, the teaching mode is 'long and uniform', the teaching content is 'one thousand people' and the teaching effect is greatly discounted; the traditional teaching mode is mainly based on a teaching mode, and experience type learning and research type learning are lacked; the traditional training is heavy in teaching light capacity, limited by teaching progress, cannot enable students to have sufficient practice and capacity cultivation, cannot formulate more targeted teaching courses according to individual differences of each student, enable students to timely 'short-board supplement', and improve understanding of theoretical knowledge through practice. How to change the defects of the traditional training, obviously improve the efficiency of the simulation training of the transformer substation, quickly adapt to the transformation and upgrading of a power grid system, maintain the national electric power safety and become the current urgent problem to be solved.
Disclosure of Invention
Aiming at the problems, the invention provides a simulation dynamic learning method and system for a transformer substation, which construct a four-level model of the capacity of operation and maintenance personnel of the transformer substation, can perform dynamic learning on the basis of the four-level capacity model, and well solve the problem of personalized learning of simulation training of the working personnel of the transformer substation.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a substation simulation dynamic learning method, including:
Constructing a four-level capacity model;
Manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
Associating the simulation training resources with the four-level capacity model, and inputting the test questions which pass the examination into a final test question library;
And responding to the instruction of successful login of the learner, and carrying out dynamic learning in coordination with the operation of the learner.
Optionally, the four-level capacity model is constructed according to a first-level specialty, a second-level capacity dimension, a third-level capacity item and a fourth-level capacity level;
The secondary capacity dimension is divided into eight categories of inspection and maintenance, switching operation, exception handling, accident handling, maintainability overhaul, equipment acceptance, basic skills and professional knowledge according to actual working requirements; each large class is divided into a plurality of capability items according to actual working business, such as primary equipment inspection, secondary equipment inspection, station direct current system inspection and the like, and finally the capability levels are divided into six levels of interns, primary workers, intermediate workers, advanced workers, technicians and advanced technicians.
Optionally, the associating the simulation training resource with the four-level capability model, and entering the test questions after the verification to a final test question library specifically includes:
after the questions are set, correlating the questions with the four-level capacity model;
Submitting the test questions to a question library to be checked;
The expert carries out examination and examination of simulation training test questions;
And submitting the examination questions which do not pass the examination, returning the examination comments to the instructor for modification, and inputting the examination questions which pass the examination into a final examination question library for the learner to log in and learn.
Optionally, the dynamically learning by the learner with successful login specifically includes:
logging in by a student, and extracting professional information of the student by a system;
The system sequentially extracts the capability level information of the students, extracts the integral of each capability item of the students, dynamically selects the capability item and dynamically generates training courses;
the learner learns to complete the current training course and acquire all the capacity points;
based on the capacity integrals, capacity grade improvement is carried out.
Optionally, the system dynamically selects the capability item, specifically including:
Selecting the ability items according to the integral of each ability item of the learner to form a random factor, and setting the random factor by using the ratio of the integral of the ability items to the integral of the threshold value of the last level;
The threshold value at each capability level is expressed as r= { R (1), R (2), … …, R (M), … …, R (M) }, R (M) being the capability threshold value of the mth term;
The capability item has obtained an integral, which can be expressed as t= { T (1), T (2), … …, T (M), … …, T (M) }, T (M) being the mth capability integral, M being the capability item label, M being the total number of capability item data contained;
The capacity integral and the threshold are denoted by T and R, respectively, and the difference D (m) = [ R (m) -T (m) ]/R (m) between them is calculated, with D (m) being the random factor for extracting the capacity term, the larger D (m) is, the greater the probability that the capacity term is extracted.
Optionally, the system dynamically generates training courses, specifically including:
each capability level has a corresponding training case, task or test question;
The system then uses rand () to generate random numbers, and randomly extracts test questions matched with the current level of the learner from training cases, tasks and test questions related to related capacity items;
the time is used as a random factor, so that the random number generated by each rand () is guaranteed to be different in large probability, and therefore, the training course generated dynamically changes along with the integral change of each capability item of the learner.
Optionally, the acquiring each capability integral specifically includes:
The learner learns to complete the current training course, acquires all the capacity points, and comprehensively evaluates the system according to the normalization of the completion of the task and the completion time to give the corresponding capacity points in the process of executing the simulation task.
Optionally, the capability level promotion specifically includes:
the learner performs course learning to obtain the capacity integral, and when all the capacity item integral under all the capacity dimensions of the professional reach the threshold value of the previous level, the level of the learner is improved;
If the training cases, tasks or test questions under the current capability level are not up to standard, the system can continue to generate dynamic test questions, and study is continued until the capability level of the students is improved, and after the capability level is improved, the system can generate matched test questions again according to the capability level of the students.
In a second aspect, the present invention provides a substation simulation dynamic learning system, including:
The construction module is used for constructing a four-level capacity model;
the manufacturing module is used for manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
The execution module is used for associating the simulation training resources with the four-level capacity model and inputting the test questions which pass the examination into a final test question library;
And the dynamic learning module is used for responding to the instruction of successful login of the learner and carrying out dynamic learning in cooperation with the operation of the learner.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a simulation dynamic learning method and system for a transformer substation, wherein a four-level model of the capacity of operation and maintenance personnel of the transformer substation is firstly constructed by the system, dynamic learning can be performed on the basis of the four-level capacity model, and the problem of personalized learning of simulation training of the working personnel of the transformer substation is well solved.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings, in which:
FIG. 1 is a schematic diagram of a training case, task or test question making process according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a process of dynamic learning by a successful log-in learner according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The principle of application of the invention is described in detail below with reference to the accompanying drawings.
Example 1
The embodiment of the invention provides a transformer substation simulation dynamic learning method, which comprises the following steps:
Constructing a four-level capacity model;
Manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
Associating the simulation training resources with the four-level capacity model, and inputting the test questions which pass the examination into a final test question library;
And responding to the instruction of successful login of the learner, and carrying out dynamic learning in coordination with the operation of the learner.
In a specific implementation of the embodiment of the present invention, the fourth-level capability model is constructed according to a first-level specialty, a second-level capability dimension, a third-level capability item, and a fourth-level capability level;
The secondary capacity dimension is divided into eight categories of inspection and maintenance, switching operation, exception handling, accident handling, maintainability overhaul, equipment acceptance, basic skills and professional knowledge according to actual working requirements; each large class is divided into a plurality of capability items according to actual working business, such as primary equipment inspection, secondary equipment inspection, station direct current system inspection and the like, and finally the capability levels are divided into six levels of interns, primary workers, intermediate workers, advanced workers, technicians and advanced technicians. As shown in the table below.
Table 1 operation and maintenance four-level capacity model of transformer substation
In a specific implementation manner of the embodiment of the present invention, as shown in fig. 1, the associating the simulation training resource with the fourth-level capability model, and entering the test questions after the verification pass into a final test question library specifically includes:
After the questions are set up (namely, training cases, tasks and test questions are made or modified), the test questions are associated with the four-level capacity model; if the simulation line is changed from running to overhauling, the simulation training test questions are associated according to a four-level capacity model, the simulation line belongs to a first-level power transformation operation and maintenance specialty, a second-level switching operation class, a third-level high-voltage switch class, a line power-off and transmission class and a four-level primary class;
Submitting the test questions to a question library to be checked;
The expert carries out examination and examination of simulation training test questions;
And submitting the examination questions which do not pass the examination, returning the examination comments to the instructor for modification, and inputting the examination questions which pass the examination into a final examination question library for the learner to log in and learn.
In a specific implementation manner of the embodiment of the present invention, as shown in fig. 2, the dynamic learning performed by the learner with successful login specifically includes:
logging in by a student, and extracting professional information of the student by a system;
The system sequentially extracts the capability level information of the students, extracts the integral of each capability item of the students, dynamically selects the capability item and dynamically generates training courses;
the learner learns to complete the current training course and acquire all the capacity points;
based on the capacity integrals, capacity grade improvement is carried out.
The system dynamically selects the capability items, which specifically comprises:
The capacity item is selected according to the random factor formed by the integral of each capacity item of the trainee, the random factor is set by the ratio of the integral of the capacity item to the integral of the threshold value of the last level, the threshold value of each capacity level is expressed as R= { R (1), R (2), … …, R (M), … …, R (M) }, and R (M) is the capacity threshold value of the mth item. The capability item has obtained an integral, which may be expressed as t= { T (1), T (2), … …, T (M), … …, T (M) }, T (M) being the mth capability integral, M being the capability item number, and M being the total number of capability item data included. The capacity integral and the threshold are denoted by T and R, respectively, and the difference D (m) = [ R (m) -T (m) ]/R (m) between them is calculated, with D (m) being the random factor for extracting the capacity term, the larger D (m) is, the greater the probability that the capacity term is extracted. The system dynamically generates training courses, which specifically comprises the following steps:
each capability level has a corresponding training case, task or test question;
The system then uses rand () to generate random numbers, and randomly extracts test questions matched with the current level of the learner from training cases, tasks and test questions related to related capacity items;
the time is used as a random factor, so that the random number generated by each rand () is guaranteed to be different in large probability, and therefore, the training course generated dynamically changes along with the integral change of each capability item of the learner.
The step of obtaining each capacity integral specifically comprises the following steps:
The learner learns to complete the current training course, acquires all the capacity points, and comprehensively evaluates the system according to the normalization of the completion of the task and the completion time to give the corresponding capacity points in the process of executing the simulation task.
The capability level improvement specifically comprises:
the learner performs course learning to obtain the capacity integral, and when all the capacity item integral under all the capacity dimensions of the professional reach the threshold value of the previous level, the level of the learner is improved;
If the training cases, tasks or test questions under the current capability level are not up to standard, the system can continue to generate dynamic test questions, and study is continued until the capability level of the students is improved, and after the capability level is improved, the system can generate matched test questions again according to the capability level of the students.
Example 2
Based on the same inventive concept as embodiment 1, in an embodiment of the present invention, there is provided a substation simulation dynamic learning system, including:
The construction module is used for constructing a four-level capacity model;
the manufacturing module is used for manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
The execution module is used for associating the simulation training resources with the four-level capacity model and inputting the test questions which pass the examination into a final test question library;
And the dynamic learning module is used for responding to the instruction of successful login of the learner and carrying out dynamic learning in cooperation with the operation of the learner.
The remainder was the same as in example 1.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The transformer substation simulation dynamic learning method is characterized by comprising the following steps of:
Constructing a four-level capacity model;
Manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
Associating the simulation training resources with the four-level capacity model, and inputting the test questions which pass the examination into a final test question library;
responding to a command that the student logs in successfully, and carrying out dynamic learning in cooperation with the operation of the student;
The successful login student carries out dynamic learning, which comprises the following steps:
logging in by a student, and extracting professional information of the student by a system;
The system sequentially extracts the capability level information of the students, extracts the integral of each capability item of the students, dynamically selects the capability item and dynamically generates training courses;
the learner learns to complete the current training course and acquire all the capacity points;
Performing capacity grade improvement based on each capacity integral;
The system dynamically selects the capability items, which specifically comprises:
Selecting the ability items according to the integral of each ability item of the learner to form a random factor, and setting the random factor by using the ratio of the integral of the ability items to the integral of the threshold value of the last level;
The threshold value at each capability level is expressed as r= { R (1), R (2), … …, R (M), … …, R (M) }, R (M) being the capability threshold value of the mth term;
The capability term has been integrated, denoted as t= { T (1), T (2), … …, T (M), … …, T (M),
T (M) is the mth capability integral, M is the capability item number, M is the total number of capability item data contained;
the capacity integral and the threshold value are respectively represented by T and R, the difference D (m) = [ R (m) -T (m) ]/R (m) between the capacity integral and the threshold value is calculated, D (m) is used as a random factor for extracting the capacity item, and the larger the D (m), the larger the possibility that the capacity item is extracted;
The system dynamically generates training courses, which specifically comprises the following steps:
each capability level has a corresponding training case, task or test question;
The system then uses rand () to generate random numbers, and randomly extracts test questions matched with the current level of the learner from training cases, tasks and test questions related to related capacity items;
the time is used as a random factor, so that the random number generated by each rand () is guaranteed to be different in large probability, and therefore, the training course generated dynamically changes along with the integral change of each capability item of the learner.
2. The substation simulation dynamic learning method according to claim 1, wherein: the four-level capacity model is constructed according to the primary specialty, the secondary capacity dimension, the tertiary capacity item and the four-level capacity level;
the secondary capacity dimension is divided into eight categories of inspection and maintenance, switching operation, exception handling, accident handling, maintainability overhaul, equipment acceptance, basic skills and professional knowledge according to actual working requirements; each large class is divided into a plurality of capability items according to actual working business, including primary equipment inspection, secondary equipment inspection, station direct current system inspection, and finally capability levels are divided into six levels of interns, primary workers, intermediate workers, advanced workers, technicians and advanced technicians.
3. The substation simulation dynamic learning method according to claim 1, wherein: the step of associating the simulation training resources with the four-level capacity model and inputting the test questions which pass the examination into a final test question library, specifically comprises the following steps:
after the questions are set, correlating the questions with the four-level capacity model;
Submitting the test questions to a question library to be checked;
The expert carries out examination and examination of simulation training test questions;
And submitting the examination questions which do not pass the examination, returning the examination comments to the instructor for modification, and inputting the examination questions which pass the examination into a final examination question library for the learner to log in and learn.
4. The substation simulation dynamic learning method according to claim 1, wherein the obtaining each capability integral specifically includes:
The learner learns to complete the current training course, acquires all the capacity points, and comprehensively evaluates the system according to the normalization of the completion of the task and the completion time to give the corresponding capacity points in the process of executing the simulation task.
5. The substation simulation dynamic learning method according to claim 4, wherein: the capability level improvement specifically comprises:
the learner performs course learning to obtain the capacity integral, and when all the capacity item integral under all the capacity dimensions of the professional reach the threshold value of the previous level, the level of the learner is improved;
If the training cases, tasks or test questions under the current capability level are not up to standard, the system can continue to generate dynamic test questions, and study is continued until the capability level of the students is improved, and after the capability level is improved, the system can generate matched test questions again according to the capability level of the students.
6. A substation simulation dynamic learning system, comprising:
The construction module is used for constructing a four-level capacity model;
the manufacturing module is used for manufacturing simulation training resources, wherein the simulation training resources comprise simulation training cases, tasks and test questions;
The execution module is used for associating the simulation training resources with the four-level capacity model and inputting the test questions which pass the examination into a final test question library;
The dynamic learning module is used for responding to the instruction of successful login of the learner and carrying out dynamic learning in cooperation with the operation of the learner;
The successful login student carries out dynamic learning, which comprises the following steps:
logging in by a student, and extracting professional information of the student by a system;
The system sequentially extracts the capability level information of the students, extracts the integral of each capability item of the students, dynamically selects the capability item and dynamically generates training courses;
the learner learns to complete the current training course and acquire all the capacity points;
Performing capacity grade improvement based on each capacity integral;
The system dynamically selects the capability items, which specifically comprises:
Selecting the ability items according to the integral of each ability item of the learner to form a random factor, and setting the random factor by using the ratio of the integral of the ability items to the integral of the threshold value of the last level;
The threshold value at each capability level is expressed as r= { R (1), R (2), … …, R (M), … …, R (M) }, R (M) being the capability threshold value of the mth term;
The capability item has obtained an integral, expressed as t= { T (1), T (2), … …, T (M), … …, T (M) }, T (M) is the mth capability integral, M is the capability item label, and M is the total number of the capability item data contained;
the capacity integral and the threshold value are respectively represented by T and R, the difference D (m) = [ R (m) -T (m) ]/R (m) between the capacity integral and the threshold value is calculated, D (m) is used as a random factor for extracting the capacity item, and the larger the D (m), the larger the possibility that the capacity item is extracted;
The system dynamically generates training courses, which specifically comprises the following steps:
each capability level has a corresponding training case, task or test question;
The system then uses rand () to generate random numbers, and randomly extracts test questions matched with the current level of the learner from training cases, tasks and test questions related to related capacity items;
the time is used as a random factor, so that the random number generated by each rand () is guaranteed to be different in large probability, and therefore, the training course generated dynamically changes along with the integral change of each capability item of the learner.
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