A PLC-based automatic adjustment debugging training system and method
By establishing a system simulation model in a thermal power plant and using I/O channels and touch screens to calculate training evaluation values, the high cost and unsustainability of relying on external experts for automatic control systems have been solved, achieving safe and efficient training and skills enhancement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUANENG POWER INT CO LTD RIZHAO POWER PLANT
- Filing Date
- 2023-09-04
- Publication Date
- 2026-05-19
AI Technical Summary
In the existing technology, the commissioning of automatic adjustment systems relies on external experts, which is costly and cannot be resolved once and for all. Furthermore, the commissioning process may affect the equipment and cause malfunctions.
By acquiring the operating parameters of the thermal process in a thermal power plant, a system simulation model is established. The user control system is connected via I/O channels for simulation debugging and training. A touch screen is used to calculate the training evaluation value. The evaluation value is then adjusted based on the training impact coefficient to determine the training level, thereby gradually improving the debugging level.
This enables debugging within a simulation system, avoiding equipment failures, improving the accuracy of training evaluation values, gradually enhancing the skills of debugging personnel, and solving the problems of high cost and unsustainability.
Smart Images
Figure CN117392888B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of debugging and training technology, and in particular to a PLC-based automatic adjustment and debugging training system and method. Background Technology
[0002] Currently, many industrial processes employ automated control systems, and the quality of these systems is highly dependent on the skill level of the personnel performing the commissioning. Companies typically hire external experts at high salaries for commissioning, but without effective maintenance, performance deteriorates over time, resulting in significant costs and failing to provide a permanent solution. To address this, it's essential to cultivate in-house experts in automated control within the company. However, training and commissioning on operating equipment is risky; incorrect parameters or logic during commissioning can severely impact the system, potentially causing equipment failure. Therefore, there is an urgent need for a training system that does not negatively affect equipment but improves the commissioning skills of company personnel. Summary of the Invention
[0003] To address the aforementioned technical problems, this application provides a PLC-based automatic adjustment and debugging training system. This system aims to solve the technical issues that require hiring external experts for automatic adjustment system debugging, which is not only costly but also fails to provide a permanent solution and can negatively impact the equipment during the debugging process.
[0004] In some embodiments of this application, operating parameters from multiple thermal processes in a thermal power plant are obtained, and the thermal processes are modeled to obtain corresponding system simulation models. The system simulation models can simulate the operating conditions of multiple thermal processes and are connected to the user's control system via I / O channels to enable debugging and training at any time. The system simulation models are independent of field equipment and are debugged entirely within the simulated system, preventing unsafe events from occurring. The I / O hard-wiring method allows connection to any type of control system, solving the problem that if parameters or logic are incorrect during training and debugging on operating equipment, it may have a significant impact on the system and may even cause equipment failure.
[0005] In some embodiments of this application, the training evaluation value is calculated and the operation process of the system simulation model by the trainees is evaluated. The training evaluation value is then corrected according to the training influence coefficient, which greatly improves the accuracy of the training evaluation value. The training level of the current trainees is determined based on the training evaluation value. This training system can gradually improve the debugging level of personnel from simple to complex, and solves the problem that the debugging of automatic adjustment systems always requires hiring external experts, which is not only costly but also cannot provide a permanent solution.
[0006] In some embodiments of this application, a PLC-based automatic adjustment, debugging, and training system is provided, comprising:
[0007] The modeling module is used to acquire operating parameters of multiple thermal processes in a thermal power plant, and to model multiple thermal processes based on the operating parameters to obtain the corresponding system simulation model;
[0008] A connection module is used to connect the system simulation model to the user's controller through a preset channel;
[0009] The control module includes a touch screen for operating the system simulation model, calculating a training evaluation value based on the trainee's operation of the system simulation model, and determining the current training level of the trainee based on the training evaluation value.
[0010] In some embodiments of this application, the system simulation model includes a single-loop regulation simulation model, a cascade regulation simulation model, a cascade three-impulse regulation simulation model, and a coordinated control simulation model.
[0011] In some embodiments of this application, the training evaluation value is calculated based on the trainee's operation procedure of the system simulation model, including:
[0012] In the system simulation model, general monitoring nodes and feature monitoring nodes are set, general data of general monitoring nodes and feature data of feature monitoring nodes are obtained, and erroneous operation data, general fault data and feature fault data are determined according to the operation process. Based on the erroneous operation data, general fault data and feature fault data, the corresponding number of erroneous operations N1, number of general fault operations N2 and number of feature fault operations N3 are determined.
[0013] The formula for calculating the training evaluation value is as follows:
[0014] ;
[0015] Where M is the training evaluation value, t1 is the duration corresponding to the number of misoperations, t2 is the duration corresponding to the number of general fault operations, t3 is the duration corresponding to the number of characteristic fault operations, n is the total number of operations, Si is the number of the i-th operation, t0i is the duration corresponding to the number of the i-th operation, N4 is the number of unpredictable faults, and y is a preset constant.
[0016] In some embodiments of this application, the control module is further configured to trace training data of the current trainees to obtain historical training data, determine historical misoperation data, historical general fault data and historical characteristic fault data based on the historical training data, determine the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3 based on the historical misoperation data, historical general fault data, historical characteristic fault data and misoperation data, general fault data and characteristic fault data, and calculate the training impact coefficient based on the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3;
[0017] The formula for calculating the training impact coefficient is as follows:
[0018] Q = K1H1 + H2K2 + H3K3;
[0019] Where Q is the training impact coefficient, H1 is the weight coefficient corresponding to the same number of misoperations, H2 is the weight coefficient corresponding to the same number of general failures, and H3 is the weight coefficient corresponding to the same number of characteristic failures.
[0020] In some embodiments of this application, the control module is used to correct the training evaluation value according to the training influence coefficient. A first preset training influence coefficient interval Q01, a second preset training influence coefficient interval Q02, a third preset training influence coefficient interval Q03, and a fourth preset training influence coefficient interval Q04 are preset, and Q01 < Q02 < Q03 < Q04. A first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3, and a fourth preset correction coefficient A4 are preset, and 0.6 < A1 < A2 < A3 < A4 < 1.
[0021] When the training impact coefficient is within the first preset training impact coefficient range, a fourth preset correction coefficient A4 is selected to correct the training evaluation value, resulting in A4 after correction. M;
[0022] When the training impact coefficient falls within the second preset training impact coefficient range, a third preset correction coefficient A3 is selected to correct the training evaluation value, resulting in A3 after correction. M;
[0023] When the training impact coefficient falls within the third preset training impact coefficient range, the second preset correction coefficient A2 is selected to correct the training evaluation value, resulting in A2 after correction. M;
[0024] When the training impact coefficient falls within the fourth preset training impact coefficient range, the first preset correction coefficient A1 is selected to correct the training evaluation value, resulting in A1 after correction. M.
[0025] In some embodiments of this application, determining the training level of the current trainee based on the training evaluation value includes:
[0026] The control module is used to preset a first training level, a second training level, a third training level and a fourth training level, and preset a first preset training evaluation value, a second preset training evaluation value and a third preset training evaluation value, wherein the first preset evaluation value is less than the second preset evaluation value and the second preset training evaluation value is less than the third preset evaluation value;
[0027] When the corrected training evaluation value is less than the first preset training evaluation value, the first training level is selected as the training level for the current trainee.
[0028] When the corrected training evaluation value is between the first preset training evaluation value and the second preset training evaluation value, the second training level is selected as the training level for the current trainee.
[0029] When the corrected training evaluation value is between the second and third preset training evaluation values, the third training level is selected as the training level for the current trainee.
[0030] When the revised training evaluation value is greater than the third preset training evaluation value, the fourth training level is selected as the training level for the current trainee.
[0031] In some embodiments of this application, when the training level of the trainee is the first training level, the trainee learns the knowledge principles of the system simulation model through the touch screen and operates according to the knowledge principles.
[0032] When the training level of the trainee is the second training level, the trainee learns the key equipment and key processes of the system simulation model through the touch screen and operates the key equipment according to the preset key data and key processes.
[0033] When the training level of the trainee is the third training level, the trainee learns the preset fault points and corresponding fault data of the system simulation model through the touch screen and operates according to the fault resolution guide.
[0034] When the trainee's training level is the fourth training level, the trainee's historical training data is obtained, and the fault settings and basic process operations for the final assessment are determined based on the historical training data. If the trainee passes the final assessment within the preset time, the training of the current trainee is completed.
[0035] In some embodiments of this application, an automatic adjustment, debugging, and training method based on a PLC is also included:
[0036] The operating parameters of multiple thermal processes in a thermal power plant are obtained, and the multiple thermal processes are modeled based on the operating parameters to obtain the corresponding system simulation model;
[0037] The system simulation model is connected to the user's controller via a preset channel;
[0038] The system simulation model is operated, and a training evaluation value is calculated based on the trainee's operation process of the system simulation model. The training level of the current trainee is determined based on the training evaluation value.
[0039] The PLC-based automatic adjustment, debugging, and training system and method described in this application have the following advantages compared to the prior art:
[0040] By acquiring operating parameters from multiple thermal processes in a thermal power plant, a corresponding system simulation model is obtained by modeling the thermal processes. The system simulation model can simulate the operating conditions of multiple thermal processes and is connected to the user's control system through I / O channels, enabling debugging and training at any time. The system simulation model is independent of the field equipment and is debugged entirely within the simulated system, preventing unsafe events from occurring. Using hard-wired I / O, it can be connected to any type of control system, solving the problem that if parameters or logic are incorrect during training and debugging on the operating equipment, it may have a significant impact on the system, and in severe cases, may cause equipment failure.
[0041] The system evaluates trainees' operation of the system simulation model and calculates training evaluation values. These values are then corrected based on the training impact coefficient, significantly improving their accuracy. The training evaluation values determine the current training level of the trainees. This training system progresses from simple to complex, catering to different levels of personnel and gradually improving their debugging skills. It solves the problem that debugging automatic adjustment systems always requires hiring external experts, which is not only costly but also not a permanent solution. Attached Figure Description
[0042] Figure 1 This is a schematic diagram of a PLC-based automatic adjustment, debugging, and training system in a preferred embodiment of this application.
[0043] Figure 2 This is a flowchart illustrating a PLC-based automatic adjustment, debugging, and training method in a preferred embodiment of this application. Detailed Implementation
[0044] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.
[0045] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.
[0046] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0047] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.
[0048] like Figure 1 As shown in the preferred embodiment of this application, a PLC-based automatic adjustment and debugging training system includes:
[0049] The modeling module is used to acquire operating parameters of multiple thermal processes in a thermal power plant, and to model multiple thermal processes based on the operating parameters to obtain the corresponding system simulation model;
[0050] A connection module is used to connect the system simulation model to the user's controller through a preset channel;
[0051] The control module includes a touch screen for operating the system simulation model, calculating a training evaluation value based on the trainee's operation of the system simulation model, and determining the current training level of the trainee based on the training evaluation value.
[0052] In this embodiment, the modeling module models the thermal process of a typical thermal power plant automatic control system in the PLC, obtaining multiple system simulation models. The relevant input / output signals are connected to the user's DCS system via I / O modules. The user programs the control process. The modeling module, connection module, and control module are electrically connected. The control module has a touchscreen interface, allowing for selection, operation, and reset of the system simulation models. After programming and system connection, the user can conduct debugging and training, fully simulating on-site conditions. The system evaluates the trainees' operational procedures and obtains training evaluation values. Based on these evaluation values, the current training level of the trainees is determined, progressing from simple to complex, allowing for a gradual and progressive improvement for different levels of personnel, ultimately cultivating expert-level talent.
[0053] In some embodiments of this application, the system simulation model includes a single-loop regulation simulation model, a cascade regulation simulation model, a cascade three-impulse regulation simulation model, and a coordinated control simulation model.
[0054] In some embodiments of this application, the training evaluation value is calculated based on the trainee's operation procedure of the system simulation model, including:
[0055] In the system simulation model, general monitoring nodes and feature monitoring nodes are set, general data of general monitoring nodes and feature data of feature monitoring nodes are obtained, and erroneous operation data, general fault data and feature fault data are determined according to the operation process. Based on the erroneous operation data, general fault data and feature fault data, the corresponding number of erroneous operations N1, number of general fault operations N2 and number of feature fault operations N3 are determined.
[0056] The formula for calculating the training evaluation value is as follows:
[0057] ;
[0058] Where M is the training evaluation value, t1 is the duration corresponding to the number of misoperations, t2 is the duration corresponding to the number of general fault operations, t3 is the duration corresponding to the number of characteristic fault operations, n is the total number of operations, Si is the number of the i-th operation, t0i is the duration corresponding to the number of the i-th operation, N4 is the number of unpredictable faults, and y is a preset constant.
[0059] In this embodiment, general monitoring nodes are points in the system simulation model where the operation has a minor impact, feature monitoring points are points in the system simulation model where the operation has a major impact, misoperation data are data and the number of times an operation is performed but does not result in a fault, general fault data are fault data and the number of times at general monitoring nodes, and feature fault data are fault data and the number of times at feature monitoring nodes.
[0060] In some embodiments of this application, the control module is further configured to trace training data of the current trainees to obtain historical training data, determine historical misoperation data, historical general fault data and historical characteristic fault data based on the historical training data, determine the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3 based on the historical misoperation data, historical general fault data, historical characteristic fault data and misoperation data, general fault data and characteristic fault data, and calculate the training impact coefficient based on the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3;
[0061] The formula for calculating the training impact coefficient is as follows:
[0062] Q = K1H1 + H2K2 + H3K3;
[0063] Where Q is the training impact coefficient, H1 is the weight coefficient corresponding to the same number of misoperations, H2 is the weight coefficient corresponding to the same number of general failures, and H3 is the weight coefficient corresponding to the same number of characteristic failures.
[0064] In this embodiment, the weighting coefficients for the same number of misoperations, the same number of general failures, and the same number of characteristic failures are 0.2, 0.3, and 0.5, respectively. The training impact coefficient is calculated based on the same number of misoperations, the same number of general failures, and the same number of characteristic failures, as well as the corresponding weighting coefficients. The training evaluation value is then corrected based on the training impact coefficient, which greatly improves the accuracy of the training evaluation value and also improves the accuracy of subsequent training level determination.
[0065] In some embodiments of this application, the control module is used to correct the training evaluation value according to the training influence coefficient. A first preset training influence coefficient interval Q01, a second preset training influence coefficient interval Q02, a third preset training influence coefficient interval Q03, and a fourth preset training influence coefficient interval Q04 are preset, and Q01 < Q02 < Q03 < Q04. A first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3, and a fourth preset correction coefficient A4 are preset, and 0.6 < A1 < A2 < A3 < A4 < 1.
[0066] When the training impact coefficient is within the first preset training impact coefficient range, a fourth preset correction coefficient A4 is selected to correct the training evaluation value, resulting in A4 after correction. M;
[0067] When the training impact coefficient falls within the second preset training impact coefficient range, a third preset correction coefficient A3 is selected to correct the training evaluation value, resulting in A3 after correction. M;
[0068] When the training impact coefficient falls within the third preset training impact coefficient range, the second preset correction coefficient A2 is selected to correct the training evaluation value, resulting in A2 after correction. M;
[0069] When the training impact coefficient falls within the fourth preset training impact coefficient range, the first preset correction coefficient A1 is selected to correct the training evaluation value, resulting in A1 after correction. M.
[0070] In some embodiments of this application, determining the training level of the current trainee based on the training evaluation value includes:
[0071] The control module is used to preset a first training level, a second training level, a third training level and a fourth training level, and preset a first preset training evaluation value, a second preset training evaluation value and a third preset training evaluation value, wherein the first preset evaluation value is less than the second preset evaluation value and the second preset training evaluation value is less than the third preset evaluation value;
[0072] When the corrected training evaluation value is less than the first preset training evaluation value, the first training level is selected as the training level for the current trainee.
[0073] When the corrected training evaluation value is between the first preset training evaluation value and the second preset training evaluation value, the second training level is selected as the training level for the current trainee.
[0074] When the corrected training evaluation value is between the second and third preset training evaluation values, the third training level is selected as the training level for the current trainee.
[0075] When the revised training evaluation value is greater than the third preset training evaluation value, the fourth training level is selected as the training level for the current trainee.
[0076] In some embodiments of this application, when the training level of the trainee is the first training level, the trainee learns the knowledge principles of the system simulation model through the touch screen and operates according to the knowledge principles.
[0077] When the training level of the trainee is the second training level, the trainee learns the key equipment and key processes of the system simulation model through the touch screen and operates the key equipment according to the preset key data and key processes.
[0078] When the trainee's training level is Level 3, they learn the preset fault locations and corresponding fault data of the system simulation model through the touch screen and operate according to the fault-solving instructions;
[0079] When the trainee's training level is the fourth training level, the trainee's historical training data is obtained, and the fault settings and basic process operations for the final assessment are determined based on the historical training data. If the trainee passes the final assessment within the preset time, the training of the current trainee is completed.
[0080] In some embodiments of this application, such as Figure 2 As shown, it also includes a PLC-based automatic adjustment and debugging training method:
[0081] Step S201: Obtain the operating parameters of multiple thermal processes in the thermal power plant, and model the multiple thermal processes based on the operating parameters to obtain the corresponding system simulation model;
[0082] Step S202: Connect the system simulation model to the user's controller through a preset channel;
[0083] Step S203: Operate the system simulation model, calculate the training evaluation value based on the trainee's operation process of the system simulation model, and determine the current training level of the trainee based on the training evaluation value.
[0084] In summary, this invention discloses a PLC-based automatic adjustment and debugging training system, comprising: a modeling module for acquiring operating parameters of multiple thermal processes in a thermal power plant, modeling the multiple thermal processes based on the operating parameters, and obtaining corresponding system simulation models; a connection module for connecting the system simulation models to a user's controller via a preset channel; and a control module including a touch screen for operating the system simulation models, evaluating the trainees' operation of the system simulation models, calculating training evaluation values, and determining the current trainee's training level based on the training evaluation values.
[0085] According to the first concept of this application, by obtaining the operating parameters of multiple thermal processes in a thermal power plant, the thermal processes are modeled to obtain the corresponding system simulation model. The system simulation model can simulate the operating conditions of multiple thermal processes and is connected to the user's control system through I / O channels to realize debugging and training at any time. The system simulation model is independent of the field equipment and is debugged entirely in the simulated system to prevent unsafe events from occurring. The I / O hard-wiring method can be connected to any type of control system, solving the problem that if the parameters or logic are incorrect during the debugging process on the operating equipment, it may have a great impact on the system and may even cause equipment failure.
[0086] According to the second concept of this application, the training evaluation value is calculated and the operation process of the system simulation model is evaluated by the trainees. The training evaluation value is then corrected according to the training influence coefficient, which greatly improves the accuracy of the training evaluation value. The training level of the current trainees is determined based on the training evaluation value. This training system can gradually improve the debugging level of personnel from simple to complex, and solves the problem that the debugging of automatic adjustment systems is always carried out by hiring external experts, which is not only costly but also cannot solve the problem once and for all.
[0087] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.
Claims
1. A PLC-based automatic adjustment and debugging training system, characterized in that, include: The modeling module is used to acquire operating parameters of multiple thermal processes in a thermal power plant, and to model multiple thermal processes based on the operating parameters to obtain the corresponding system simulation model; A connection module is used to connect the system simulation model to the user's controller through a preset channel; The control module includes a touch screen, which is used to operate the system simulation model, calculate a training evaluation value based on the trainee's operation process of the system simulation model, and determine the current training level of the trainee based on the training evaluation value. The training evaluation value is calculated based on the trainees' operation procedures of the system simulation model, including: In the system simulation model, general monitoring nodes and feature monitoring nodes are set, general data of general monitoring nodes and feature data of feature monitoring nodes are obtained, and erroneous operation data, general fault data and feature fault data are determined according to the operation process. Based on the erroneous operation data, general fault data and feature fault data, the corresponding number of erroneous operations N1, number of general fault operations N2 and number of feature fault operations N3 are determined. The formula for calculating the training evaluation value is as follows: ； Where M is the training evaluation value, t1 is the duration corresponding to the number of misoperations, t2 is the duration corresponding to the number of general fault operations, t3 is the duration corresponding to the number of characteristic fault operations, n is the total number of operations, Si is the number of the i-th operation, t0i is the duration corresponding to the number of the i-th operation, N4 is the number of unpredictable faults, and y is a preset constant. The control module is also used to trace training data of current trainees to obtain historical training data, determine historical misoperation data, historical general fault data and historical characteristic fault data based on historical training data, determine the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3 based on historical misoperation data, historical general fault data, historical characteristic fault data and misoperation data, general fault data and characteristic fault data, and calculate the training impact coefficient based on the number of identical misoperations K1, the number of identical general faults K2 and the number of identical characteristic faults K3; The formula for calculating the training impact coefficient is as follows: Q = K1H1 + H2K2 + H3K3; Where Q is the training impact coefficient, H1 is the weight coefficient corresponding to the same number of misoperations, H2 is the weight coefficient corresponding to the same number of general failures, and H3 is the weight coefficient corresponding to the same number of characteristic failures. The control module is used to correct the training evaluation value according to the training impact coefficient. It has a first preset training impact coefficient interval Q01, a second preset training impact coefficient interval Q02, a third preset training impact coefficient interval Q03, and a fourth preset training impact coefficient interval Q04, where Q01 < Q02 < Q03 < Q04. It also has a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3, and a fourth preset correction coefficient A4, where 0.6 < A1 < A2 < A3 < A4 < 1. When the training impact coefficient is within the first preset training impact coefficient range, a fourth preset correction coefficient A4 is selected to correct the training evaluation value, resulting in A4 after correction. M; When the training impact coefficient falls within the second preset training impact coefficient range, a third preset correction coefficient A3 is selected to correct the training evaluation value, resulting in A3 after correction. M; When the training impact coefficient falls within the third preset training impact coefficient range, the second preset correction coefficient A2 is selected to correct the training evaluation value, resulting in A2 after correction. M; When the training impact coefficient falls within the fourth preset training impact coefficient range, the first preset correction coefficient A1 is selected to correct the training evaluation value, resulting in A1 after correction. M.
2. The PLC-based automatic adjustment, debugging, and training system as described in claim 1, characterized in that, The system simulation models include a single-loop regulation simulation model, a cascade regulation simulation model, a cascade three-impulse regulation simulation model, and a coordinated control simulation model.
3. The PLC-based automatic adjustment, debugging, and training system as described in claim 1, characterized in that, The training level of the current trainee is determined based on the training evaluation value, including: The control module is used to preset a first training level, a second training level, a third training level and a fourth training level, and preset a first preset training evaluation value, a second preset training evaluation value and a third preset training evaluation value, wherein the first preset evaluation value is less than the second preset evaluation value and the second preset training evaluation value is less than the third preset evaluation value; When the corrected training evaluation value is less than the first preset training evaluation value, the first training level is selected as the training level for the current trainee. When the corrected training evaluation value is between the first preset training evaluation value and the second preset training evaluation value, the second training level is selected as the training level for the current trainee. When the corrected training evaluation value is between the second and third preset training evaluation values, the third training level is selected as the training level for the current trainee. When the revised training evaluation value is greater than the third preset training evaluation value, the fourth training level is selected as the training level for the current trainee.
4. The PLC-based automatic adjustment, debugging, and training system as described in claim 3, characterized in that, When the trainee's training level is the first training level, they learn the knowledge and principles of the system simulation model through the touch screen and operate according to the guidance of the knowledge and principles. When the training level of the trainee is the second training level, the trainee learns the key equipment and key processes of the system simulation model through the touch screen and operates the key equipment according to the preset key data and key processes. When the training level of the trainee is the third training level, the trainee learns the preset fault points and corresponding fault data of the system simulation model through the touch screen and operates according to the fault resolution guide. When the trainee's training level is the fourth training level, the trainee's historical training data is obtained, and the fault settings and basic process operations for the final assessment are determined based on the historical training data. If the trainee passes the final assessment within the preset time, the training of the current trainee is completed.
5. A PLC-based automatic adjustment and debugging training method, characterized in that, include: The operating parameters of multiple thermal processes in a thermal power plant are obtained, and the multiple thermal processes are modeled based on the operating parameters to obtain the corresponding system simulation model; The system simulation model is connected to the user's controller via a preset channel; The system simulation model is operated, and a training evaluation value is calculated based on the trainee's operation process of the system simulation model. The training level of the current trainee is determined based on the training evaluation value. The training evaluation value is calculated based on the trainees' operation procedures of the system simulation model, including: In the system simulation model, general monitoring nodes and feature monitoring nodes are set, general data of general monitoring nodes and feature data of feature monitoring nodes are obtained, and erroneous operation data, general fault data and feature fault data are determined according to the operation process. Based on the erroneous operation data, general fault data and feature fault data, the corresponding number of erroneous operations N1, number of general fault operations N2 and number of feature fault operations N3 are determined. The formula for calculating the training evaluation value is as follows: ； Where M is the training evaluation value, t1 is the duration corresponding to the number of misoperations, t2 is the duration corresponding to the number of general fault operations, t3 is the duration corresponding to the number of characteristic fault operations, n is the total number of operations, Si is the number of the i-th operation, t0i is the duration corresponding to the number of the i-th operation, N4 is the number of unpredictable faults, and y is a preset constant. The control module is also used to trace training data of current trainees to obtain historical training data. Based on the historical training data, it determines historical misoperation data, historical general failure data, and historical characteristic failure data. Based on the historical misoperation data, historical general failure data, historical characteristic failure data, and misoperation data, general failure data, and characteristic failure data, it determines the number of identical misoperations K1, the number of identical general failures K2, and the number of identical characteristic failures K3. Based on the number of identical misoperations K1, the number of identical general failures K2, and the number of identical characteristic failures K3, it calculates the training impact coefficient. The formula for calculating the training impact coefficient is as follows: Q = K1H1 + H2K2 + H3K3; Where Q is the training impact coefficient, H1 is the weight coefficient corresponding to the same number of misoperations, H2 is the weight coefficient corresponding to the same number of general failures, and H3 is the weight coefficient corresponding to the same number of characteristic failures. The control module is used to correct the training evaluation value according to the training impact coefficient. It has a first preset training impact coefficient interval Q01, a second preset training impact coefficient interval Q02, a third preset training impact coefficient interval Q03, and a fourth preset training impact coefficient interval Q04, where Q01 < Q02 < Q03 < Q04. It also has a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3, and a fourth preset correction coefficient A4, where 0.6 < A1 < A2 < A3 < A4 < 1. When the training impact coefficient is within the first preset training impact coefficient range, a fourth preset correction coefficient A4 is selected to correct the training evaluation value, resulting in A4 after correction. M; When the training impact coefficient falls within the second preset training impact coefficient range, a third preset correction coefficient A3 is selected to correct the training evaluation value, resulting in A3 after correction. M; When the training impact coefficient falls within the third preset training impact coefficient range, the second preset correction coefficient A2 is selected to correct the training evaluation value, resulting in A2 after correction. M; When the training impact coefficient falls within the fourth preset training impact coefficient range, the first preset correction coefficient A1 is selected to correct the training evaluation value, resulting in A1 after correction. M.