Teaching practical scene simulation control method based on PLC technology

Abstract

The application belongs to the technical field of teaching practical training scene simulation control, and specifically discloses a teaching practical training scene simulation control method based on PLC technology, which comprises the following steps: constructing a fault mode library containing low, medium and high levels, randomly distributing faults from the low fault level, loading a simulation scene, collecting fault identification response time and adjustment operation sequence, verifying diagnosis accuracy and evaluating fault recovery degree after executing the sequence, generating a practical operation qualification degree according to different conditions, then judging promotion and dynamically adjusting the fault level according to the qualification degree, and generating a student comprehensive ability evaluation report based on the qualification degrees in the previous iterations; the application constructs a fault mode library containing low, medium and high fault levels based on historical power operation data, and dynamically adjusts the fault level to be distributed subsequently according to the practical operation qualification degree, thereby overcoming the defects of static preset fault situations, and realizing a personalized and gradual skill upgrading path.

Description

Technical Field

[0001] This invention belongs to the field of teaching and training scenario simulation control technology, and relates to a teaching and training scenario simulation control method based on PLC technology. Background Technology

[0002] PLCs are the core of industrial automation control systems. In related professional teaching, constructing control environments that closely resemble industrial scenarios through simulation technology has become a key means of cultivating PLC programming and fault diagnosis capabilities. This method simulates the controlled object through software, forming a closed loop with a physical or virtual PLC, enabling students to complete program debugging and fault diagnosis without physical equipment, effectively overcoming the limitations of high cost and high risk associated with real equipment.

[0003] For example, a virtual simulation teaching and training system, control method, device and storage medium disclosed in CN113327473A. This system obtains the knowledge points involved in the teaching experiment, determines the corresponding teaching situation, role information and related object information accordingly, and drives the transformation of object state and situation background through the student's answer results in the role simulation process, thereby realizing contextualized teaching based on knowledge points.

[0004] The existing technologies mentioned above have the following shortcomings: 1. Most of the current fault scenarios are preset static configurations, lacking a structured fault mode library based on historical data and covering multiple difficulty levels. At the same time, they cannot dynamically adjust the allocation of subsequent fault types and levels according to the students' real-time operation performance, which leads to a mismatch between the training content and the students' actual abilities, making it difficult to achieve personalized advanced training.

[0005] 2. The current assessment system mainly relies on the answer results to trigger scenario changes. The assessment dimensions are single and do not cover key process indicators such as fault identification response time, diagnostic accuracy, and key parameter recovery. As a result, it is impossible to accurately identify students' shortcomings in operational efficiency, fault judgment accuracy, and system recovery standardization, making it difficult to provide targeted teaching feedback. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a simulation control method for teaching and training scenarios based on PLC technology is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a teaching and training scenario simulation control method based on PLC technology, including: S1, constructing a fault mode library containing three fault levels: low, medium, and high, based on historical power operation data.

[0008] S2. Randomly assign target fault types from the low fault levels in the fault mode library, load the simulation scenario, and collect the students' fault identification response time and fault adjustment operation sequence.

[0009] S3. Execute the fault adjustment operation sequence, collect adjustment operation data, verify the accuracy of fault diagnosis and evaluate fault recovery, and obtain the fault diagnosis accuracy and fault recovery rate.

[0010] S4. Based on the accuracy of fault diagnosis and the degree of fault recovery, determine whether the fault recovery qualification conditions are met. If they are met, generate the student's qualification for practical training operation at the low fault level based on the fault identification response time, fault diagnosis accuracy and fault recovery degree. If they are not met, generate the qualification for practical training operation based on the fault identification response time, fault diagnosis accuracy and fault adjustment operation sequence.

[0011] S5. Based on the qualification of the training operation, determine whether the promotion conditions are met, and dynamically adjust the fault level of the subsequent assigned target fault type accordingly.

[0012] S6. Iterate through S2 to S5, and generate a comprehensive ability assessment report for students based on the pass rate of each practical training operation generated during the iteration process.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a fault mode library containing three fault levels (low, medium and high) based on historical power operation data, and dynamically adjusts the fault level assigned afterward according to the qualification of practical training operation, thereby overcoming the defects of static preset fault scenarios, enabling the training content to match the actual ability of students, and thus realizing a personalized and progressive skill advancement path.

[0014] (2) This invention introduces multi-dimensional process indicators such as fault identification response time, diagnostic accuracy, fault recovery degree and sequence matching degree into the evaluation mechanism, and generates a comprehensive practical training operation qualification based on these indicators. This allows for the accurate identification of students' shortcomings in operational efficiency, judgment accuracy and operational standardization, providing a reliable basis for subsequent targeted teaching improvements.

[0015] (3) The present invention obtains the fault recovery degree by weighted fusion of the benchmark recovery degree and the average relative deviation of the abnormal key operating parameters, and sets a composite qualification condition that includes parameter deviation and overall recovery degree, thereby realizing the fine quantification of the system recovery state, avoiding the coarseness of traditional binary judgment, and guiding students to pay attention to the accuracy of parameter adjustment.

[0016] (4) This invention achieves automation and intelligence in the training process through a closed-loop process of fault allocation, scenario loading, data collection, evaluation and verification, qualification generation, promotion judgment and iterative execution, thereby ensuring close connection between training, evaluation and promotion links and continuously driving the improvement of students' abilities. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention.

[0019] Figure 2 This is a schematic diagram showing the connection steps for constructing the fault mode library of the present invention.

[0020] Figure 3 This is a schematic diagram showing the connection steps of the fault recovery assessment in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a simulation control method for teaching and training scenarios based on PLC technology. The method includes: S1, constructing a fault mode library containing three fault levels: low, medium and high, based on historical power operation data.

[0023] Please see Figure 2 As shown, for example, the construction of a fault mode library containing three fault levels (low, medium, and high) includes: obtaining the number of fault-related devices for each fault type from historical power operation data, arranging the fault types in descending order of the number of fault-related devices, and dividing them into high, medium, and low complexity levels according to a preset ratio.

[0024] Considering that the number of devices associated with a fault directly reflects the scope of its impact and is an important indicator for assessing its complexity, classifying faults by arranging the devices in descending order of their number and combining this with a preset ratio can objectively distinguish the differences in the scale of handling the faults. In a specific embodiment of this invention, based on a preset ratio derived from historical data statistical analysis or teaching experience, for example, the top 20% of fault types are classified as high complexity, the middle 50% as medium complexity, and the bottom 30% as low complexity, thereby effectively distinguishing fault complexity and ensuring the objectivity of the classification.

[0025] In a preferred embodiment of the present invention, the fault types include, but are not limited to, power phase loss fault, motor overload fault, grounding fault, communication interruption fault, sensor drift fault, contactor sticking fault, and PLC module fault.

[0026] The number of parameter types required for fault location is obtained from historical power operation data. Fault types are then sorted in descending order of the number of parameter types and categorized into high, medium, and low diagnostic difficulty levels based on a preset ratio. Considering that the number of monitored parameter types reflects the information dimensions and diagnostic depth required for fault location and is a crucial basis for assessing fault diagnosis difficulty, classifying faults by the number of parameter types accurately distinguishes the cognitive load requirements during the fault diagnosis process.

[0027] Based on the complexity level and diagnostic difficulty level of each fault type, if there is a high level in either the complexity level or the diagnostic difficulty level, then the fault type is classified as a high fault level.

[0028] If both the complexity level and the diagnostic difficulty level are low, the fault type is classified as a low fault level; otherwise, the fault type is classified as a medium fault level.

[0029] Based on this, the aforementioned dual-dimensional quantitative evaluation method, combined with a fault level classification achieved through preset proportions, ensures the objectivity and repeatability of the classification process, providing a scientific basis for the accurate matching of subsequent training content. Based on this grading system, the training system can dynamically adjust the difficulty of faults according to the students' actual ability level, avoiding the frustration experienced by novice students when facing complex faults, and solving the problem of low efficiency caused by advanced students repeatedly training on simple content.

[0030] S2. Randomly assign target fault types from the low fault levels in the fault mode library, load the simulation scenario, and collect the students' fault identification response time and fault adjustment operation sequence.

[0031] S3. Execute the fault adjustment operation sequence, collect adjustment operation data, verify the accuracy of fault diagnosis and evaluate fault recovery, and obtain the fault diagnosis accuracy and fault recovery rate.

[0032] For example, the verification of the accuracy of fault diagnosis includes: obtaining the fault points identified by the students during the diagnosis phase from the adjusted operation data.

[0033] Each fault point is compared with the baseline fault point corresponding to the target fault type. The number of matching fault points and the number of baseline fault points are counted, and the ratio of the two is used as the fault diagnosis accuracy.

[0034] Please see Figure 3As shown, for example, the fault recovery assessment includes: obtaining the operating values ​​of each key operating parameter from the adjusted operating data, and comparing them with the normal operating threshold range corresponding to the target fault type.

[0035] The normal operating threshold range corresponding to a fault type refers to the allowable stable fluctuation range of key operating parameters when a specific device or system is in a healthy and fault-free state. This range defines the boundary conditions for normal system operation and is the core benchmark for judging whether the operating state has recovered. The normal operating threshold range is obtained by directly acquiring the rated values ​​of the parameters and their allowable tolerance ranges from the manufacturer's technical manual, design drawings, or performance specifications of the device or PLC control system.

[0036] The number of key operating parameters that have recovered to the normal operating threshold range is counted, and the ratio of this number to the total number of key operating parameters is calculated as the baseline recovery rate.

[0037] The critical operating parameters that have not recovered to the normal operating threshold range are recorded as abnormal critical operating parameters. The absolute difference between the operating value of each abnormal critical operating parameter and the boundary value of the normal operating threshold is calculated and divided by the width of the normal operating threshold range to obtain the relative deviation.

[0038] The relative deviation is specifically calculated using the following formula: , Indicates the first The relative deviation of a key operating parameter that has not been restored. Indicates the first The actual operating values ​​of key operating parameters that have not been restored in the adjusted operating data. Indicates the first The boundary value closest to the actual value within the normal operating threshold range corresponding to each unrecovered critical operating parameter is considered the lower limit of the range if the actual value is below the normal range, and the upper limit of the range if the actual value is above the normal range. Indicates the first The width of the normal operating threshold range for a critical operating parameter that has not been restored, i.e., the difference between the upper and lower limits.

[0039] The principle behind this formula is that by normalizing the absolute difference relative to the threshold range, it eliminates the influence of different parameters due to differences in dimensions and normal fluctuation range, making the deviation between different parameters comparable and providing a scientific basis for subsequent calculation of the average relative deviation.

[0040] The average relative deviation is calculated by averaging the relative deviations, and the fault recovery is obtained by weighted fusion of the baseline recovery and the average relative deviation.

[0041] It should be added that the weighted fusion calculation formula for the fault recovery degree is as follows: In the formula For fault recovery rate, As the baseline recovery, The average relative deviation. and These are the weights for fault recovery and accuracy recovery, respectively. The accuracy recovery rate is calculated by subtracting the average relative deviation from the value of 1. Accuracy recovery rate quantifies the system's ability to bring abnormally critical operating parameters closer to their normal threshold range. The average relative deviation reflects the average degree of misalignment of the abnormally critical operating parameters; the smaller the value, the closer the parameter is to the normal range. Therefore, accuracy recovery rate becomes a positive indicator; the larger the value, the higher the parameter recovery accuracy.

[0042] The above method can be used to reflect the different emphases of the overall system recovery and the precise recovery of local parameters on the fault recovery effect by weight allocation, and can directly integrate the information of the macro-level recovery effect and the micro-level parameter control accuracy, and comprehensively consider the overall impact of the two on the fault recovery quality.

[0043] The weights can be set based on the characteristics of the fault type and the requirements for safe operation of the system, or they can be obtained through historical operating data. For example, historical data on the baseline recovery degree, average relative deviation degree and corresponding fault recovery effect during the system recovery process can be collected first. The correlation coefficient between the two and the final recovery effect evaluation can be calculated. The contribution of the two to the fault recovery quality can be determined through regression analysis. After normalization, the contribution degree is transformed into the weight of the baseline recovery degree and the accuracy recovery degree, so as to accurately quantify the fault recovery degree.

[0044] S4. Based on the accuracy of fault diagnosis and the degree of fault recovery, determine whether the fault recovery qualification conditions are met. If they are met, generate the student's qualification for practical training operation at the low fault level based on the fault identification response time, fault diagnosis accuracy and fault recovery degree. If they are not met, generate the qualification for practical training operation based on the fault identification response time, fault diagnosis accuracy and fault adjustment operation sequence.

[0045] Specifically, step S4 employs different evaluation paths based on whether the fault recovery qualification conditions are met. Its technical logic and educational value lie in constructing a dynamic evaluation mechanism that combines outcome-oriented and process-diagnostic approaches. When the student's operation meets the fault recovery qualification conditions, it indicates that the system has successfully recovered from the fault state, and key operating parameters are all within safe and normal threshold ranges. At this point, the student's operation sequence has achieved the core objective, and the evaluation focus should shift from whether the process is correct to the quality of the execution effect. Specifically: the accuracy of fault diagnosis quantifies the student's precision in locating the fault point; the degree of fault recovery comprehensively evaluates the overall system recovery and the accuracy of parameter adjustment; and the fault identification response time reflects the student's response and handling efficiency to the fault.

[0046] The advantage of this assessment method lies in its ability to guide students beyond basic task completion, encouraging them to pursue more efficient, accurate, and high-quality system recovery, aligning with the training principles of skill advancement. Simultaneously, its assessment dimensions highly match the requirements of industrial practice, truly reflecting the comprehensive ability of engineers to balance efficiency, reliability, and risk control when troubleshooting. Furthermore, the comprehensive evaluation based on operational excellence provides a reliable basis for the system's adaptive advancement mechanism, thereby more accurately determining whether students possess the ability to handle more complex faults.

[0047] When a student's operation fails to meet the criteria for successful fault recovery, it indicates that the system has failed to return to normal operation, and the operation is considered a failure at the outcome level. At this point, the core task of system evaluation is to diagnose the root cause of the failure, rather than merely determining the result. Therefore, the evaluation focus shifts to an in-depth analysis of the standardization of the operational process. Specifically: By assessing the accuracy of fault diagnosis, it determines whether the failure stemmed from an initial error in fault location. By analyzing the fault adjustment operation sequence, it analyzes whether, even with correct fault location, chaotic, omitted, or sequential operational steps led to recovery failure or even secondary problems. By assessing the fault identification response time, it evaluates the student's alertness and initial reaction capabilities, which remain valuable even in failed operations.

[0048] By accurately identifying students' specific deviations in the standardization of operating procedures, targeted improvement feedback is provided. At the same time, students' awareness of safe and standardized operation in industrial environments is strengthened. Furthermore, based on their persistent weaknesses, specialized training modules that match them can be automatically recommended, thereby realizing a closed-loop teaching intervention from problem diagnosis to awareness cultivation and then to personalized and precise reinforcement.

[0049] For example, determining whether the fault recovery qualification conditions are met includes: comparing the relative deviation of each abnormal key operating parameter with a preset relative deviation threshold, and comparing the fault recovery degree with a preset fault recovery degree qualification threshold.

[0050] The preset relative deviation threshold is a critical value used to determine whether the deviation of a single abnormal key operating parameter is acceptable. This threshold is set to ensure that all abnormal key operating parameters are controlled to a state that, while not fully recovered, is very close to the normal range, avoiding secondary system anomalies or potential risks caused by severe deviations of individual parameters. It is obtained by analyzing the acceptable transient deviation upper limit of parameters outside the normal fluctuation range from historical normal operating data, and taking its statistical quantile as the threshold, for example, the 95th percentile.

[0051] The preset fault recovery qualification threshold is the minimum standard used to determine whether the overall system recovery effect meets the requirements. This threshold comprehensively considers the baseline recovery degree and the accuracy recovery degree of the system recovery, representing the minimum health level that the system should reach after fault handling is completed. It is obtained by: based on industry maintenance standards in the field of the industrial control system or by joint confirmation by field experts based on the system's stable operation requirements.

[0052] Condition 1 is defined as the relative deviation of all abnormal key operating parameters not exceeding the preset relative deviation threshold, and condition 2 is defined as the fault recovery rate reaching or exceeding the preset fault recovery rate qualification threshold.

[0053] If both conditions 1 and 2 are met, the fault recovery qualification is deemed to be met; otherwise, the fault recovery qualification is deemed not to be met.

[0054] For example, when the fault recovery qualification condition is met, the generation of the training operation qualification includes: comparing the fault identification response time with the benchmark fault identification response time interval corresponding to the target fault type, wherein the benchmark fault identification response time interval is set based on the average response time ± standard deviation of excellent students under the same fault in historical training data, and the excellent students refer to the group of students whose training operation qualification is qualified in their comprehensive ability assessment report and whose average training operation qualification ranks in the top 20%.

[0055] Statistical time efficiency coefficient , In the formula, For fault identification response time, and These are the lower and upper limits of the baseline fault identification response time interval, respectively. The baseline fault identification response time interval length is [length], and the baseline fault identification response time interval length is [length]. .

[0056] When the response time is within the range of the baseline fault identification response time, it indicates that the time efficiency is optimal, and the time efficiency coefficient is 1.

[0057] When the response time exceeds the baseline fault identification response time range, its efficiency coefficient decreases linearly with the degree of deviation from the baseline fault identification response time range. (The formula...) and The relative deviation from the length of the baseline fault identification response time interval was calculated.

[0058] use The function ensures that the time efficiency coefficient will not become negative due to excessive deviation, and its minimum value is 0. This conforms to the basic properties of the coefficient and avoids introducing unreasonable extreme effects in subsequent weighted calculations.

[0059] The time efficiency coefficient, fault diagnosis accuracy, and fault recovery rate are weighted and integrated to obtain the student's pass rate in practical training at low fault levels.

[0060] It should be added that the student's qualification score for low-fault-level practical training is generated based on whether the fault recovery qualification conditions are met. If the fault recovery qualification conditions are met, the qualification score is generated based on fault identification response time, fault diagnosis accuracy, and fault recovery rate. The weighted fusion calculation formula for the qualification score is as follows: In the formula To ensure the pass rate of practical training operations, This is the time efficiency coefficient. To improve the accuracy of fault diagnosis, For fault recovery rate, , and These are the weights of time efficiency coefficient, fault diagnosis accuracy, and fault recovery rate, respectively, under the condition that fault recovery is qualified, and they satisfy... .

[0061] The pass rate for practical training operations is obtained through weighted fusion calculation, focusing on evaluating the overall effectiveness of students in problem-solving. Among these, the time efficiency coefficient reflects response speed, fault diagnosis accuracy reflects fault location precision, and fault recovery rate assesses the system's recovery capability. For example, weights can be set based on teaching objectives and skill requirements, or obtained from historical training data. In one embodiment of the invention, the weights are... , , The teaching focus is on demonstrating fault diagnosis capabilities.

[0062] The weights can be set according to the teaching objectives and skills training requirements, or they can be obtained from historical training data. For example, first collect the time efficiency coefficient, fault diagnosis accuracy, fault recovery rate, and corresponding training operation qualification rate of students in each training session, calculate the correlation coefficient between each indicator and the final training operation qualification rate, determine the contribution of each indicator to the comprehensive ability through multiple regression analysis, and after normalization, convert the contribution into the weight of each indicator, with the total weight being 1.

[0063] For example, when the fault recovery qualification conditions are not met, the generation of the training operation qualification degree includes: parsing the student's fault adjustment operation sequence into a standardized operation sequence in chronological order, and matching and analyzing it with the standard diagnostic process template corresponding to the target fault type to obtain the sequence matching degree.

[0064] Among them, the standard diagnostic process template corresponding to the target fault type refers to the predefined and standardized diagnostic and recovery operation sequence benchmark for a specific fault type.

[0065] Furthermore, the analysis of the sequence matching degree includes: obtaining the first position index of each key step from the standard diagnostic process template, and retrieving whether each key step exists in the standardized operation sequence.

[0066] If the critical step exists in the standardized operation sequence, its second position index in the standardized operation sequence is recorded, and the absolute difference between the first position index and the second position index is calculated as the position offset of the critical step.

[0067] If the critical step does not exist in the standardized operation sequence, the position offset of the critical step is set to the length of the standardized operation sequence.

[0068] The average position offset is obtained by averaging the position offsets of all critical steps.

[0069] Divide the average position offset by the total number of critical steps to obtain the relative order deviation. Then subtract the relative order deviation from the value 1 to obtain the order matching degree.

[0070] In one specific embodiment, a standard procedure and student operations are first defined: a standard diagnostic procedure template is set to contain 5 key steps, with their first position indices being: {Step A:1, Step B:2, Step C:3, Step D:4, Step E:5}. A student's standardized operation sequence is: [Step B, Step A, Step D, Step F], with a sequence length L=4.

[0071] Next, calculate the position offsets of each key step: Step A exists in the sequence, second position index = 2, position offset = |1-2| = 1. Step B exists in the sequence, second position index = 1, position offset = |2-1| = 1. Step C does not exist in the sequence, position offset = sequence length = 4. Step D exists in the sequence, second position index = 3, position offset = |4-3| = 1. Step E does not exist in the sequence, position offset = sequence length = 4.

[0072] Finally, calculate the order matching degree: average position offset. Relative deviation of order Order matching degree .

[0073] This embodiment demonstrates the complete calculation process of sequence matching degree. By comparing the key steps of the standard procedure with the student's actual operation sequence, the standardization of the student's operation procedure can be quantitatively evaluated. When a student omits a key step or disrupts the operation order, the sequence matching degree will decrease accordingly, thus objectively reflecting the student's deficiencies in the standardization of the operation procedure.

[0074] Based on the time efficiency coefficient, fault diagnosis accuracy, and sequence matching degree, the qualification rate of the practical training operation is generated by weighted calculation.

[0075] It should be added that the weighted fusion calculation formula for the pass rate of the practical training operation is as follows: In the formula To ensure the pass rate of practical training operations, For order matching degree, , and These are the weights of time efficiency coefficient, fault diagnosis accuracy, and sequence matching degree, respectively, under the condition that fault recovery is not qualified, and they satisfy... .

[0076] This weighted fusion calculation focuses on assessing the standardization of the student's diagnostic process. When the final recovery outcome fails to meet the target, the assessment shifts to the degree of adherence to the standard diagnostic procedure. Weights can be set based on teaching objectives or obtained from historical data. In one embodiment of the invention, the weights are... , , This is to emphasize the importance of standardized operating procedures.

[0077] The weights can be set according to the teaching objectives and skills training requirements, or they can be obtained from historical training data. For example, first collect the students' sequence matching degree, time efficiency coefficient, fault diagnosis accuracy and corresponding teacher comprehensive score data in each training session, calculate the correlation coefficient between each indicator and the final score, determine the contribution of each indicator to the comprehensive ability through multiple regression analysis, and after normalization, convert the contribution into the weight of each indicator, with the total weight being 1.

[0078] S5. Based on the qualification of the training operation, determine whether the promotion conditions are met, and dynamically adjust the fault level of the subsequent assigned target fault type accordingly.

[0079] For example, determining whether the advancement conditions are met includes: comparing the student's pass rate in the current fault level with the pass rate threshold corresponding to the fault level to determine whether each training operation is qualified, and counting the number of consecutive qualified training operations of the student in the current fault level.

[0080] The pass / fail threshold corresponding to the fault level refers to the minimum passing standard that a student must achieve in their practical training operation at a specific fault level. This threshold is dynamically adjusted according to different fault levels, reflecting the gradual increase in operational skill requirements as the difficulty of the fault increases.

[0081] The method for obtaining the pass / fail threshold includes, but is not limited to, a combination of one or more of the following methods: The first method is to set a graded threshold based on historical performance data. Pass / fail data for low, medium, and high fault levels are extracted from the historical training data of students at each fault level, and the median of each level's data is used as the pass / fail threshold for that level. For example, the low fault level threshold can be taken as the 60th percentile of historical pass / fail data, with the threshold increasing sequentially for medium and high levels.

[0082] The second approach is based on progressively setting teaching objectives, using a progressive standard according to the competency requirements for different fault levels in the teaching syllabus. For example, the pass / fail threshold is set at 0.70 for low fault level, 0.75 for medium fault level, and 0.80 for high fault level, to reflect the gradually stricter skill requirements as the level increases.

[0083] The number of consecutive qualified training operations performed by the student in the current fault level is compared with the promotion threshold corresponding to the fault level. When the number of consecutive qualified training operations performed by the student in the current fault level reaches or exceeds the promotion threshold, it is determined that the promotion condition is met; otherwise, it is determined that the promotion condition is not met.

[0084] It should be added that the advancement threshold corresponding to the fault level refers to the minimum number of consecutive successful completions of practical training required for a student at a specific fault level before moving on to higher-difficulty training. This threshold increases in stages according to different fault levels, reflecting the gradually strengthening stability requirements as skill levels improve. The advancement threshold is obtained by adopting a tiered standard based on the progressive requirements for skill stability in teaching principles. For example, a low fault level requires two consecutive successful completions, a medium fault level requires three consecutive successful completions, and a high fault level requires four consecutive successful completions, to reflect the gradual increase in operational stability requirements as the level increases.

[0085] S6. Iterate through S2 to S5, and generate a comprehensive ability assessment report for students based on the pass rate of each practical training operation generated during the iteration process.

[0086] For example, generating a comprehensive ability assessment report for students includes: obtaining the student's pass rate and failure level in each practical training operation from the assessment results of each practical training operation, and then counting the total number of qualified practical training operations with high failure levels.

[0087] When the total number of qualified training operations at high fault levels reaches or exceeds the preset threshold for the total number of qualified training operations, the student's training operation is deemed qualified; otherwise, the student's training operation is deemed unqualified.

[0088] Based on the results of the students' practical training operation qualification assessment and the students' qualification rate in each practical training operation, a comprehensive ability assessment report of the students is generated.

[0089] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0091] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0094] Finally, the above description is only a preferred embodiment of the present invention and is 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 simulation control method for teaching and training scenarios based on PLC technology, characterized in that: The method includes: S1. Construct a fault mode library containing three fault levels: low, medium, and high, based on historical power operation data; S2. Randomly assign target fault types from the low fault levels in the fault mode library, load the simulation scenario, and collect the students' fault identification response time and fault adjustment operation sequence. S3. Execute the fault adjustment operation sequence, collect adjustment operation data, verify the accuracy of fault diagnosis and evaluate fault recovery, and obtain the fault diagnosis accuracy and fault recovery rate. The fault recovery assessment includes: The operating values ​​of each key operating parameter are obtained from the adjusted operating data, and then compared with their corresponding normal operating threshold ranges under the target fault type. The number of key operating parameters that have recovered to the normal operating threshold range is counted, and the ratio of this number to the total number of key operating parameters is calculated as the baseline recovery rate. The critical operating parameters that have not recovered to the normal operating threshold range are recorded as abnormal critical operating parameters. The absolute difference between the operating value of each abnormal critical operating parameter and the boundary value of the normal operating threshold is calculated and divided by the width of the normal operating threshold range to obtain the relative deviation. The average relative deviation is calculated by averaging the relative deviations, and the fault recovery is obtained by weighted fusion of the baseline recovery and the average relative deviation. S4. Based on the accuracy of fault diagnosis and the degree of fault recovery, determine whether the fault recovery qualification conditions are met. If they are met, generate the student's qualification for practical training operation at the low fault level based on the fault identification response time, fault diagnosis accuracy and fault recovery degree. If they are not met, generate the qualification for practical training operation based on the fault identification response time, fault diagnosis accuracy and fault adjustment operation sequence. S5. Based on the qualification of the training operation, determine whether the promotion conditions are met, and dynamically adjust the fault level of the subsequent assigned target fault type accordingly. S6. Iterate through S2 to S5, and generate a comprehensive ability assessment report for students based on the pass rate of each practical training operation generated during the iteration process.

2. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: The construction of the fault mode library, which includes low, medium, and high fault levels, includes: The number of fault-related devices for each fault type is obtained from historical power operation data. The fault types are sorted in descending order by the number of fault-related devices and classified into high, medium and low complexity levels according to a preset ratio. The number of parameter types required to locate each fault type is obtained from historical power operation data. The fault types are sorted in descending order by the number of parameter types and divided into high, medium and low diagnostic difficulty levels according to a preset ratio. Based on the complexity level and diagnostic difficulty level of each fault type, if there is a high level in either the complexity level or the diagnostic difficulty level, then the fault type is classified as a high fault level. If both the complexity level and the diagnostic difficulty level are low, the fault type is classified as a low fault level; otherwise, the fault type is classified as a medium fault level.

3. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: The verification of the accuracy of fault diagnosis includes: Obtain the fault points identified by students during the diagnostic phase from the adjusted operational data; Each fault point is compared with the baseline fault point corresponding to the target fault type. The number of matching fault points and the number of baseline fault points are counted, and the ratio of the two is used as the fault diagnosis accuracy.

4. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: The determination of whether the fault recovery qualification conditions are met includes: The relative deviation of each abnormal key operating parameter is compared with the preset relative deviation threshold, and the fault recovery degree is compared with the preset fault recovery degree qualified threshold. Condition 1 is that the relative deviation of all abnormal key operating parameters does not exceed the preset relative deviation threshold, and condition 2 is that the fault recovery reaches or exceeds the preset fault recovery qualification threshold. If both conditions 1 and 2 are met, the fault recovery qualification is deemed to be met; otherwise, the fault recovery qualification is deemed not to be met.

5. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: When the fault recovery qualification conditions are met, the qualification of the generated training operation includes: Compare the fault identification response time with the baseline fault identification response time range corresponding to the target fault type; Statistical time efficiency coefficient , In the formula, For fault identification response time, and These are the lower and upper limits of the baseline fault identification response time interval, respectively. The length of the baseline fault identification response time interval; The time efficiency coefficient, fault diagnosis accuracy, and fault recovery rate are weighted and integrated to obtain the student's pass rate in practical training at low fault levels.

6. The teaching and training scenario simulation control method based on PLC technology according to claim 5, characterized in that: When the fault recovery qualification conditions are not met, the qualification of the generated training operation includes: The student's fault adjustment operation sequence is parsed into a standardized operation sequence according to the time order, and then matched and analyzed with the standard diagnostic process template corresponding to the target fault type to obtain the sequence matching degree. Based on the time efficiency coefficient, fault diagnosis accuracy, and sequence matching degree, the qualification rate of the practical training operation is generated by weighted calculation.

7. The teaching and training scenario simulation control method based on PLC technology according to claim 6, characterized in that: The analysis of the order matching degree includes: Obtain the first position index of each key step from the standard diagnostic process template, and retrieve whether each key step exists in the standardized operation sequence; If the critical step exists in the standardized operation sequence, record its second position index in the standardized operation sequence, and calculate the absolute difference between the first position index and the second position index as the position offset of the critical step. If the critical step does not exist in the standardized operation sequence, the position offset of the critical step is set to the length value of the standardized operation sequence. The average position offset is obtained by averaging the position offsets of all critical steps. Divide the average position offset by the total number of critical steps to obtain the relative order deviation. Then subtract the relative order deviation from the value 1 to obtain the order matching degree.

8. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: The determination of whether the advancement conditions are met includes: Compare the student's pass rate in the practical training operation at the current fault level with the pass rate threshold corresponding to the fault level to determine whether each practical training operation is qualified, and count the number of consecutive qualified practical training operations of the student at the current fault level. The number of consecutive qualified training operations performed by the student in the current fault level is compared with the promotion threshold corresponding to the fault level. When the number of consecutive qualified training operations performed by the student in the current fault level reaches or exceeds the promotion threshold, it is determined that the promotion condition is met; otherwise, it is determined that the promotion condition is not met.

9. The teaching and training scenario simulation control method based on PLC technology according to claim 1, characterized in that: The generated comprehensive ability assessment report for students includes: The pass rate and failure level of students in each practical training operation are obtained from the evaluation results of students in each practical training operation, and then the total number of students in the high failure level of the qualified practical training operation is counted. When the total number of qualified training operations at high fault levels reaches or exceeds the preset threshold for the total number of qualified training operations, the student's training operation is deemed qualified; otherwise, the student's training operation is deemed unqualified. Based on the results of the students' practical training operation qualification assessment and the students' qualification rate in each practical training operation, a comprehensive ability assessment report of the students is generated.

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