Intelligent auxiliary patrol method and device for nuclear power plant state parameters

Abstract

The application provides a nuclear power plant state parameter intelligent auxiliary patrol pre-warning response method and device, the method comprises the following steps: monitoring the intelligent auxiliary patrol system, when the intelligent auxiliary patrol system sends out a "first warning" of a state parameter abnormality of a unit, judging whether the "first warning" is a false warning based on a false warning judgment condition, if yes, prompting system abnormality or unsuitable working condition, if no, going to the next step; starting a parameter trend prediction function of the intelligent auxiliary patrol system, developing trend prediction is carried out on the abnormal state parameter, monitoring the intelligent auxiliary patrol system, when the intelligent auxiliary patrol system sends out a "second warning" of the state parameter abnormality, going to the next step; starting an auxiliary decision information function of the intelligent auxiliary patrol system, judging the category of the consequence risk according to the information provided by the auxiliary decision information function module, and executing corresponding preparation work. The application can improve the accuracy and efficiency of the patrol work in the main control room, and ensure the safe and stable operation of the power plant.

Description

Technical Field

[0001] This invention specifically relates to a method and device for intelligent auxiliary inspection and early warning response of nuclear power plant status parameters. Background Technology

[0002] To ensure that the nuclear power plant's operating status meets the requirements of technical specifications, guidelines, and relevant operating procedures, operators need to monitor the changes in various operating parameters of the unit in real time. Currently, most newly built pressurized water reactor nuclear power plants adopt a Digital Control System (DCS). Operators complete the nuclear power plant's operation monitoring tasks by monitoring the "alarm system" on the main control room computer workstation (OWP) and performing regular monitoring and recording based on various monitoring interfaces on the workstation screen (i.e., "regular panel checks"). The "alarm system" is mainly used to issue audible and visual alarms after the power plant's operating status parameters exceed the "alarm threshold" (after a fault occurs), prompting the operator to pay attention. The "regular panel checks" are performed by the operator before the power plant's operating status parameters exceed the aforementioned "alarm threshold," by monitoring and recording the changing trends of various status parameters in real time through the panel monitoring interface on the main control room workstation screen, understanding the overall operating status of the unit, and preventing the unit from entering a faulty operating condition.

[0003] Nuclear power plant systems are massive, with numerous monitoring parameters. When monitoring a large number of fluctuating status parameters during random unit operation, the workload for control room operators is heavy, demanding significant time and energy, leading to high levels of fatigue and increasing the risk of human error, such as missed or incorrect assessments due to failure to promptly and effectively identify abnormal parameter trends. After detecting anomalies in status parameters, it is necessary to assess the potential impact of the anomaly on the safe operation of the nuclear power plant as early as possible, based on the experience and knowledge of the operators, and to formulate operation and maintenance strategies based on the anticipated impact. However, the complexity of nuclear power units makes it difficult for human knowledge, experience, and reliability to meet the accuracy and timeliness requirements of assessing the impact trends of abnormal states in nuclear power plants.

[0004] To improve the efficiency of control room operators' inspection work in nuclear power plants, reduce the workload of operating personnel, avoid human error, and improve the accuracy and timeliness of assessments of unit anomalies, a data-driven intelligent auxiliary inspection system needs to be deployed next to the control room workstation in a pressurized water reactor nuclear power plant. This system will assist control room operators in their inspection work without affecting the operation of the power plant's DCS system. The intelligent auxiliary inspection system primarily provides early warning and trend risk prediction for six types of state parameters—temperature, pressure, liquid level, flow rate, pump speed, and effective vibration values—in 22 key process systems, including the reactor coolant system, under normal operating conditions. The intelligent auxiliary inspection system has two levels of warnings. When the residual between the system's predicted state curve and the measured state curve exceeds the dynamic warning threshold range, a "Level 1 warning" is triggered. When the trend prediction curve of the parameter corresponding to the "Level 1 warning" shows a risk of exceeding the "alarm threshold" within 30 minutes, a "Level 2 warning" is triggered. When both "Level 1" and "Level 2" warnings are triggered, the system will issue audible and visual alerts to prompt the operator to pay attention.

[0005] Practice has shown that simply integrating the intelligent auxiliary monitoring system into the power plant's main control room operating system and using it to assist operators in monitoring unit status parameters results in a vague response strategy for abnormal warnings from the intelligent auxiliary monitoring system. This approach cannot effectively reduce the workload of operators or prevent human error. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art by providing a method for intelligent auxiliary inspection and early warning response of nuclear power plant status parameters to improve the accuracy and efficiency of the main control room inspection work and ensure the safe and stable operation of the power plant. The invention also provides a device for implementing the method.

[0007] The technical solution adopted to solve the technical problem of this invention is:

[0008] This invention provides a method for intelligent auxiliary monitoring and early warning response of nuclear power plant status parameters, including:

[0009] S1: Monitor the intelligent auxiliary inspection system. When it issues a "Level 1 warning" indicating an abnormality in a certain status parameter of the unit, based on the false warning discrimination criteria, determine whether the "Level 1 warning" from the intelligent auxiliary inspection system is a false warning. If so, indicate that the intelligent auxiliary inspection system is malfunctioning or the operating condition is unsuitable. If not, proceed to step S2.

[0010] S2: Activate the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of abnormal status parameters, monitor the intelligent assisted inspection system, and when it issues a "Level 2 warning" for the abnormal status parameters, proceed to step S3.

[0011] S3: Activate the auxiliary decision-making information function of the intelligent auxiliary inspection system. Based on the information provided by the auxiliary decision-making information function module, generate corresponding preparatory work according to the category of consequences and risks, determine the category of consequences and risks, and execute the corresponding preparatory work.

[0012] Optionally, in step S1, based on the false warning discrimination criteria, it is determined whether the "Level 1 Warning" of the intelligent assisted inspection system is a false warning. If so, it indicates that the intelligent assisted inspection system is abnormal or the operating condition is unsuitable; if not, proceed to step S2, which specifically includes:

[0013] S1.1: Determine whether the unit is operating under normal operating conditions applicable to the intelligent auxiliary inspection system. If yes, proceed to step S1.2. If no, indicate that the "Level 1 warning" is a false warning and the intelligent auxiliary inspection system is not applicable.

[0014] S1.2: Determine whether the warning threshold of the intelligent assisted inspection system is abnormal. If not, proceed to step S1.3. If yes, indicate that the "Level 1 warning" is a false warning and the intelligent assisted inspection system is unavailable due to the abnormal warning threshold.

[0015] S1.3: Determine if there is a data quality problem. If yes, indicate that the "Level 1 Warning" is a false warning and the intelligent auxiliary inspection system is unavailable due to low data quality. If no, proceed to step S2.

[0016] Optionally, in step S1.3, the data quality issues include invalid erroneous data, missing data, and data acquisition noise.

[0017] Optionally, in step S1.2, after determining that the warning threshold of the intelligent assisted inspection system is abnormal, the method further includes: determining whether the model training data does not cover the applicable working conditions; if so, indicating that the "Level 1 warning" is a false warning; if not, determining whether the system model is not sufficiently trained; if so, indicating that the "Level 1 warning" is a false warning; if not, proceeding to step S1.2.

[0018] Optionally, in step S1.2, after indicating that the "Level 1 Warning" is a false warning, the following steps are also included:

[0019] The intelligent assisted inspection system was maintained and updated. After confirming that the updated system functions normally, the updated intelligent assisted inspection system was put into operation.

[0020] Optionally, in step S3, determining the category of the consequence risk and performing corresponding preparatory work specifically includes:

[0021] S3.1: Determine whether the "alarm threshold" that the abnormal state parameter is expected to exceed is the entry point for the unit's accident procedure. If yes, perform the preparatory work for entering the accident procedure; otherwise, proceed to step S3.2.

[0022] S3.2: Determine whether the "alarm threshold" expected to be exceeded by the abnormal state parameter corresponds to the operating restrictions in the operating technical specifications. If yes, prepare for corrective measures; otherwise, proceed to step S3.3.

[0023] S3.3: Determine whether the expected exceedance of the abnormal state parameter's "alarm threshold" will trigger an automatic interlocking action. If yes, perform preparatory work for the automatic interlocking action; otherwise, proceed to step S3.4.

[0024] S3.4: Based on the preventive maintenance plan in the auxiliary decision-making information function, perform the corresponding preventive maintenance work.

[0025] Optionally, in step S3.1, after performing the preparatory work for entering the accident procedure, the method further includes: monitoring whether the DCS system issues an alarm indicating that the status parameter is abnormal; if so, executing the accident procedure according to the alarm procedure file corresponding to the abnormal status parameter; if not, continuing to perform the preparatory work for entering the accident procedure.

[0026] In step S3.2, after the preparation work for the corrective measures is performed, the method further includes: monitoring whether the DCS system issues an alarm indicating that the status parameter is abnormal. If so, the corresponding corrective measures are performed according to the alarm procedure document corresponding to the abnormal status parameter. If not, the preparation work for the corrective measures continues.

[0027] In step S3.3, after performing the preparation work for the automatic interlocking action, the method further includes: monitoring whether the DCS system issues an alarm indicating that the status parameter is abnormal. If so, performing the corresponding maintenance operation according to the alarm procedure file corresponding to the abnormal status parameter; if not, continuing to perform the preparation work for the automatic interlocking action.

[0028] This invention also provides an intelligent auxiliary monitoring and early warning response device for nuclear power plant status parameters, comprising: a monitoring module, a primary early warning response module, and a secondary early warning response module.

[0029] The monitoring module is used to monitor the intelligent auxiliary inspection system and transmit the "Level 1 Early Warning" signal issued by the system indicating an abnormality in a certain status parameter of the unit to the Level 1 Early Warning Response module.

[0030] The Level 1 Early Warning Response Module is used to, upon receiving a Level 1 Early Warning signal indicating abnormal status parameters, determine whether the Level 1 Early Warning is a false warning based on false warning discrimination criteria. If so, it indicates that the intelligent assisted inspection system is malfunctioning or the operating condition is unsuitable. If not, it activates the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of the abnormal status parameters.

[0031] The monitoring module is also used to transmit the "Level 2 Early Warning" signal indicating abnormal status parameters issued by the intelligent assisted inspection system to the Level 2 Early Warning Response module.

[0032] The Level 2 Early Warning Response Module is used to activate the auxiliary decision-making information function of the intelligent auxiliary inspection system when it receives a Level 2 Early Warning signal indicating abnormal status parameters. It generates corresponding preparatory work based on the category of consequences and risks, determines the category of consequences and risks, and executes the corresponding preparatory work.

[0033] Optionally, the first-level early warning response module includes:

[0034] The first-level judgment module is used to determine whether the unit is operating under the normal operating conditions applicable to the intelligent auxiliary inspection system.

[0035] The first alert module is used to indicate that the "Level 1 Warning" is a false warning and the intelligent auxiliary inspection system is not applicable if the first-level judgment module determines that the unit is not operating under normal operating conditions applicable to the intelligent auxiliary inspection system.

[0036] The second-level judgment module is used to determine whether the warning thresholds of the intelligent auxiliary inspection system are abnormal when the unit is operating under the normal operating conditions applicable to the intelligent auxiliary inspection system, as determined by the first-level judgment module.

[0037] The second prompt module is used to indicate that the "Level 1 Warning" is a false warning and the intelligent assisted patrol system is unavailable due to the abnormal warning threshold when the Level 2 judgment module determines that the warning threshold of the intelligent assisted patrol system is abnormal.

[0038] The third-level judgment module is used to determine whether there are data quality issues when the second-level judgment module determines that the early warning threshold of the intelligent assisted inspection system is normal.

[0039] The third alert module is used to indicate that a "Level 1 Warning" is a false warning when the third-level judgment module determines that the intelligent assisted inspection system has data quality problems. This means the intelligent assisted inspection system is unavailable due to low data quality.

[0040] The first startup module is used to start the parameter trend prediction function of the intelligent auxiliary inspection system when the third-level judgment module determines that there is no data quality problem in the intelligent auxiliary inspection system, and to predict the development trend of abnormal status parameters.

[0041] Optionally, the Level 2 early warning response module includes:

[0042] The second startup module is used to activate the auxiliary decision-making information function of the intelligent assisted inspection system when a "level two early warning" signal indicating abnormal status parameters is received.

[0043] The generation module is used to generate corresponding preparatory work based on the information provided by the decision support information function module, according to the category of consequence risk.

[0044] The first and second level judgment modules are used to determine whether the "alarm threshold" that the abnormal state parameter is expected to exceed is the entry point of the unit's accident procedure.

[0045] The first execution module is used to perform preparatory work for entering the accident procedure when the first and second level judgment modules determine that the abnormal status parameters are expected to exceed the "alarm threshold" and that this is the entry point for the unit accident procedure.

[0046] The second-level judgment module is used to determine whether the "alarm threshold" expected to be exceeded by the abnormal state parameter corresponds to the operating restrictions in the operating technical specifications when the first-level judgment module determines that the "alarm threshold" is not the entry point of the unit accident procedure.

[0047] The second execution module is used to prepare corrective measures when the second and third level judgment modules determine that the abnormal status parameters are expected to exceed the "alarm threshold" corresponding to the operational limitations in the operating technical specifications.

[0048] The third-level judgment module is used to determine whether an automatic chain reaction will be triggered when the abnormal state parameter is expected to exceed the "alarm threshold" as determined by the second-level judgment module, which does not correspond to the operating restrictions in the operating technical specifications.

[0049] The third execution module is used to prepare for automatic chain actions when the third and second-level judgment modules determine that the abnormal state parameters are expected to exceed the "alarm threshold," which will trigger automatic chain actions.

[0050] The fourth execution module is used to perform corresponding preventive maintenance work based on the preventive maintenance plan in the auxiliary decision information function when the third and second level judgment modules determine that the abnormal status parameters expected to exceed the "alarm threshold" will not trigger automatic chain actions.

[0051] This invention enables an intelligent auxiliary inspection system for nuclear power plant control rooms to provide early warning responses. It not only effectively identifies false alarms, preventing them and improving the accuracy of control room inspections, but also predicts trends and assesses risks related to abnormal plant conditions. Combined with decision-making information from early warning parameters, it assists operators in developing comprehensive maintenance strategies, efficiently ensuring the safe and stable operation of the plant and preventing units from entering faulty operating conditions. Therefore, this invention improves the efficiency of control room operators' inspection work, reduces the workload of operating personnel, avoids human error, and enhances the accuracy and timeliness of assessments of abnormal unit conditions. Furthermore, this invention has good applicability and is suitable for use in conjunction with intelligent auxiliary inspection systems in various types of nuclear power plants. Attached Figure Description

[0052] Figure 1 The flowchart is a process for the intelligent assisted monitoring and early warning response method for nuclear power plant status parameters provided in Embodiment 1 of the present invention. Detailed Implementation

[0053] The technical solutions of the invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without creative effort are within the scope of the invention.

[0054] In the description of this invention, it should be noted that the use of terms such as "above" to indicate orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings and is only for the purpose of facilitating and simplifying the description. It does 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, and therefore should not be construed as a limitation of this invention.

[0055] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0056] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connection," "setting," "installation," "fixing," etc., 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 direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0057] This invention provides a method for intelligent auxiliary monitoring and early warning response of nuclear power plant status parameters, including:

[0058] S1: Monitor the intelligent auxiliary inspection system. When it issues a "Level 1 warning" indicating an abnormality in a certain status parameter of the unit, based on the false warning discrimination criteria, determine whether the "Level 1 warning" from the intelligent auxiliary inspection system is a false warning. If so, indicate that the intelligent auxiliary inspection system is malfunctioning or the operating condition is unsuitable. If not, proceed to step S2.

[0059] S2: Activate the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of abnormal status parameters, monitor the intelligent assisted inspection system, and when it issues a "Level 2 warning" for the abnormal status parameters, proceed to step S3.

[0060] S3: Activate the auxiliary decision-making information function of the intelligent auxiliary inspection system. Based on the information provided by the auxiliary decision-making information function module, generate corresponding preparatory work according to the category of consequences and risks, determine the category of consequences and risks, and execute the corresponding preparatory work.

[0061] This invention also provides an intelligent auxiliary monitoring and early warning response device for nuclear power plant status parameters, comprising: a monitoring module, a primary early warning response module, and a secondary early warning response module.

[0062] The monitoring module is used to monitor the intelligent auxiliary inspection system and transmit the "Level 1 Early Warning" signal issued by the system indicating an abnormality in a certain status parameter of the unit to the Level 1 Early Warning Response module.

[0063] The Level 1 Early Warning Response Module is used to, upon receiving a Level 1 Early Warning signal indicating abnormal status parameters, determine whether the Level 1 Early Warning is a false warning based on false warning discrimination criteria. If so, it indicates that the intelligent assisted inspection system is malfunctioning or the operating condition is unsuitable. If not, it activates the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of the abnormal status parameters.

[0064] The monitoring module is also used to transmit the "Level 2 Early Warning" signal indicating abnormal status parameters issued by the intelligent assisted inspection system to the Level 2 Early Warning Response module.

[0065] The Level 2 Early Warning Response Module is used to activate the auxiliary decision-making information function of the intelligent auxiliary inspection system when it receives a Level 2 Early Warning signal indicating abnormal status parameters. It generates corresponding preparatory work based on the category of consequences and risks, determines the category of consequences and risks, and executes the corresponding preparatory work.

[0066] Example 1:

[0067] The following problems exist when operators in the main control room use the intelligent auxiliary monitoring system to monitor the status parameters of the nuclear power plant's process systems:

[0068] 1) When the intelligent auxiliary inspection system issues an abnormal warning of unit status parameters, the operator needs to conduct cause analysis on the warning information. However, relevant experience shows that the intelligent auxiliary inspection system may issue false alarms. How should we confirm that the abnormal warning is a "real warning"?

[0069] 2) During normal unit operation, the application of the intelligent auxiliary monitoring system in the main control room will change the existing nuclear power plant's operation and maintenance decision-making strategies. Regarding the "real warning" of unit status parameters triggered by the intelligent auxiliary monitoring system, how should operation and maintenance decisions be made in conjunction with the impact assessment of abnormal unit conditions to efficiently ensure the safe operation of the power plant and prevent the unit from entering a faulty operating condition?

[0070] Therefore, it is essential to integrate the intelligent auxiliary monitoring system into the monitoring workflow of power plant control room operators and adjust the relevant operation and maintenance response strategies accordingly to fully utilize the system's functionality. A key challenge is how to verify the authenticity of abnormal unit status parameter warnings from the intelligent auxiliary monitoring system and then combine this with an impact assessment of the abnormal unit conditions to make operational and maintenance decisions.

[0071] To ensure efficient and accurate response to abnormal unit operating parameter warnings issued by the intelligent auxiliary monitoring system during monitoring operations by operators in the main control room of a nuclear power plant, thereby assisting operators in operational and maintenance decisions, improving the efficiency of monitoring operations, and avoiding human error, this embodiment proposes a response method for intelligent auxiliary monitoring warnings in the main control room of a pressurized water reactor nuclear power plant, based on the functions of the intelligent auxiliary monitoring system and relevant experience in monitoring operations in the main control room. Figure 1 As shown, it includes:

[0072] S1: Monitor the intelligent auxiliary inspection system. When it issues a "Level 1 warning" indicating an abnormality in a certain status parameter of the unit, based on the false warning discrimination criteria, determine whether the "Level 1 warning" from the intelligent auxiliary inspection system is a false warning. If so, indicate that the intelligent auxiliary inspection system is malfunctioning or the operating condition is unsuitable. If not, proceed to step S2.

[0073] S2: Activate the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of abnormal status parameters, monitor the intelligent assisted inspection system, and when it issues a "Level 2 warning" for abnormal status parameters, proceed to step S3.

[0074] S3: Activate the auxiliary decision-making information function of the intelligent auxiliary inspection system. Based on the information provided by the auxiliary decision-making information function module, generate corresponding preparatory work according to the category of consequences and risks, determine the category of consequences and risks, and execute the corresponding preparatory work.

[0075] In summary, this invention enables an intelligent auxiliary inspection system for nuclear power plant control rooms to provide early warning responses. It not only effectively identifies false alarms, preventing them and improving the accuracy of control room inspections, but also predicts trends and assesses risks related to abnormal plant conditions. Combined with decision-making information from early warning parameters, it assists operators in developing comprehensive maintenance strategies, efficiently ensuring safe plant operation and preventing units from entering faulty operating conditions. Therefore, this invention improves the efficiency of control room operators' inspection work, reduces the workload of operating personnel, avoids human error, and enhances the accuracy and timeliness of assessing the impact of abnormal unit conditions. Furthermore, this invention has good applicability and is suitable for use in conjunction with intelligent auxiliary inspection systems in various types of nuclear power plants.

[0076] In this embodiment,

[0077] In step S1, based on the false alarm discrimination criteria, it is determined whether the "Level 1 Warning" of the intelligent assisted inspection system is a false alarm. If so, it indicates that the intelligent assisted inspection system is abnormal or the operating condition is unsuitable. If not, proceed to step S2, which specifically includes:

[0078] S1.1: Determine whether the unit is operating under normal operating conditions applicable to the intelligent auxiliary inspection system. If yes, proceed to step S1.2. If no, indicate that the "Level 1 warning" is a false warning and the intelligent auxiliary inspection system is not applicable.

[0079] If the system operates outside of its applicable normal operating conditions, the operating data at this time is not known or available to the system and may be identified as abnormal by the system, triggering a "false warning". At this time, the system will display the message "Intelligent Assisted Inspection System is not applicable".

[0080] S1.2: Determine whether the warning threshold of the intelligent assisted inspection system is abnormal. If not, proceed to step S1.3. If yes, indicate that the "Level 1 warning" is a false warning and the intelligent assisted inspection system is unavailable due to the abnormal warning threshold.

[0081] If the warning threshold of the intelligent assisted inspection system is too tight, it will become overly sensitive to anomalies, triggering false alarms even when the parameters are in a steady-state and normal operating condition. In this case, the system model engineer will further analyze the cause of the intelligent assisted inspection system failure (e.g., the system model training data does not cover all applicable operating conditions, the system model training is insufficient, etc.), and maintain and update the system according to the cause of the failure until the system function is restored to normal.

[0082] S1.3: Determine if there is a data quality problem. If yes, indicate that the "Level 1 Warning" is a false warning and the intelligent auxiliary inspection system is unavailable due to low data quality. If no, proceed to step S2.

[0083] If data quality issues occur during the data collection process, including invalid or erroneous data, missing data, and data collection noise, a "false alarm" will be triggered. The data quality issues need to be addressed and repaired before the system is restarted and brought back online.

[0084] In this embodiment,

[0085] In step S1.2, after determining that the warning threshold of the intelligent assisted inspection system is abnormal, the following steps are also included: determining whether the model training data does not cover the applicable working conditions. If so, the system will indicate that the "Level 1 warning" is a false warning. If not, the system model will determine whether it is not sufficiently trained. If so, the system will indicate that the "Level 1 warning" is a false warning. If not, the process will proceed to step S1.2.

[0086] In step S2, for the confirmed "real warning", the parameter trend prediction function of the intelligent assisted inspection system is activated to predict the development trend of the warning parameters; at the same time, the intelligent assisted inspection system continuously tracks the development trend prediction curve of the parameters. When there is a risk that the parameter will exceed the "alarm threshold" within 30 minutes, the intelligent assisted inspection system will trigger a "level two warning".

[0087] In step S3, the category of the consequence risk is determined, and the corresponding preparatory work is carried out, specifically including:

[0088] S3.1: Determine whether the "alarm threshold" that the abnormal state parameter is expected to exceed is the entry point for the unit's accident procedure. If yes, perform the preparatory work for entering the accident procedure; otherwise, proceed to step S3.2.

[0089] S3.2: Determine whether the "alarm threshold" expected to be exceeded by the abnormal state parameter corresponds to the operating restrictions in the operating technical specifications. If yes, prepare for corrective measures; otherwise, proceed to step S3.3.

[0090] S3.3: Determine whether the expected exceedance of the abnormal state parameter's "alarm threshold" will trigger an automatic interlocking action. If yes, perform preparatory work for the automatic interlocking action; otherwise, proceed to step S3.4.

[0091] S3.4: Based on the preventive maintenance plan in the auxiliary decision-making information function, perform the corresponding preventive maintenance work.

[0092] After determining the category of the consequences and risks, the first step is to take maintenance measures to prevent the parameters from deteriorating as much as possible, while preparing for the possibility that the parameters will deteriorate beyond the threshold and lead to an accident.

[0093] In step S3.1, based on the alarm response procedure in the early warning parameter-assisted decision information, it is determined whether the early warning parameter is the entry point for the unit's accident procedure. If it is indeed the entry point, preparations for entering the accident procedure must be made in advance, and measures should be taken as promptly as possible to prevent the parameter from deteriorating and avoid the unit entering an accident operating condition. After executing the preparation work for entering the accident procedure, it also includes: monitoring whether the DCS system issues an alarm for abnormal status parameters. If so, the accident procedure is executed according to the alarm procedure file corresponding to the abnormal status parameters; if not, the preparation work for entering the accident procedure continues.

[0094] In step S3.2, based on the operational technical specification document, it is determined whether the warning parameters correspond to the Limits on Operation (LCO) conditions in the operational technical specification. If they are indeed LCO conditions, preparations must be made in advance to implement corrective measures (even unit shutdown) according to LCO requirements, and measures should be taken as promptly as possible to prevent parameter deterioration and avoid safety issues caused by the unit deviating from normal operating conditions. After the preparation work for implementing corrective measures, it also includes: monitoring whether the DCS system issues an alarm for abnormal status parameters. If so, the corresponding corrective measures are implemented according to the alarm procedure document corresponding to the abnormal status parameters; if not, the preparation work for implementing corrective measures continues.

[0095] In step S3.3, based on the alarm response procedure in the early warning parameter-assisted decision information, it is determined whether the early warning parameter will trigger an automatic interlocking action. If it will trigger a series of automatic interlocking actions, the impact of each interlocking action on the unit's operating status must be assessed first, relevant preparatory work should be done in advance, and measures should be taken as promptly as possible to prevent parameter deterioration and avoid automatic unit actions affecting the safe and stable operation of the unit. After executing the preparatory work for the automatic interlocking action, it also includes: monitoring whether the DCS system issues an alarm for abnormal status parameters. If so, the corresponding maintenance operation is performed according to the alarm procedure document corresponding to the abnormal status parameters; if not, the preparatory work for the automatic interlocking action continues.

[0096] The auxiliary inspection system involved in this invention includes:

[0097] The data access module connects to the unit's digital instrumentation and control system (DCS) in real time through the nuclear power plant's office network PI database to obtain the nuclear power plant unit's status parameters and operating data.

[0098] The discrimination module is used to determine whether the unit is in the power operation condition under the first preset condition based on the preset unit operation condition discrimination conditions;

[0099] The intelligent detection module is used to identify anomalies in the monitoring status parameters during the operation of the nuclear power plant when the discrimination module determines that the unit meets the first preset power operation condition, and to obtain the monitoring results of the status parameters of each system of the nuclear power plant.

[0100] The trend prediction module is used to make long-term predictions of the development and change trends of abnormal parameters of nuclear power units based on the inspection and monitoring results, and to obtain the trend prediction results of abnormal parameters.

[0101] The information push module is used to push auxiliary decision-making information corresponding to abnormal parameters of the unit based on the trend prediction results.

[0102] The auxiliary inspection system is also known as the intelligent auxiliary inspection system or intelligent inspection system. The PI database in the nuclear power plant's office network is connected to and stores the DCS data of the unit in real time. The intelligent auxiliary inspection system obtains the operating data of the main status parameters of the key process system from the power plant's PI database in real time through the data access module to support the display of relevant application content on the intelligent auxiliary inspection system terminal.

[0103] In addition to process data, design data is also input during data access. Process data refers to the real-time monitoring data of the main status parameters of the key process systems of the nuclear power plant, while design data refers to the design documents such as alarm response procedures and preventive maintenance plans corresponding to the main status parameters of the key process systems of the nuclear power plant. After input, the data is stored in the auxiliary inspection system.

[0104] The discrimination module determines whether the unit is in a power operation condition that meets the first preset condition and is applicable to the system, based on the unit operation condition discrimination conditions in the nuclear power plant operation technical documents. The power operation condition that meets the first preset condition can be set to a power operation condition of more than 25%. If the unit is in other operating conditions, the system will automatically exit operation.

[0105] The intelligent detection module, based on intelligent technologies such as the self-associative neural network algorithm, constructs an intelligent monitoring model for the main inspection parameters of the nuclear power plant. It obtains the state estimates of the parameters as a monitoring benchmark. By setting a dynamic threshold band for the difference between the measured value and the state estimate of the parameters, it realizes timely and accurate identification of anomalies in the main inspection state parameters during the operation of the nuclear power plant. It obtains the inspection monitoring results of the main state parameters of the key process system of the nuclear power plant, including the real-time monitoring curve of the parameters, the system "level one early warning" signal, the abnormal parameter tag number and the time of the anomaly.

[0106] The trend prediction module is based on intelligent technologies such as long short-term memory neural network algorithms to construct a time-series prediction model for the main monitoring parameters of the nuclear power plant. Based on the historical data of the parameters, it obtains the development trend of the next 30 minutes and realizes the long-term prediction of the development and change trend of abnormal parameters of nuclear power units, including the prediction curve of the development trend of abnormal parameter operating status, the time when the expected exceedance of design limits is possible, and the system's "secondary early warning" signal.

[0107] The information push module pushes auxiliary decision-making information related to abnormal parameters of the unit, including information such as the cause of the abnormality and handling measures involved in the alarm response procedure document, as well as information on whether there is a relevant maintenance plan in the unit's preventive maintenance plan.

[0108] Furthermore, the auxiliary inspection system also includes an application module. Based on basic data and intelligent analysis algorithms, the application module enables the application of an intelligent auxiliary inspection system for the main status parameters of key process systems in nuclear power plants through the visualization output and interaction of the system terminal interface. This includes unit inspection status monitoring and risk trend prediction of abnormal parameters.

[0109] The auxiliary monitoring system primarily functions to identify early anomalies in the key status parameters of critical process systems in nuclear power plants, acting as a "Level 1 warning." When a slight deviation occurs in the dynamic operating trend of the key status parameters of these systems, the intelligent monitoring system's "Level 1 warning" is triggered, issuing a light-colored audible and visual alert to proactively notify the control room operator, thus improving the operator's monitoring efficiency.

[0110] A trend risk prediction function is set up for abnormal state parameters identified by the system, serving as the system's "secondary early warning". When the main state parameters of the critical process system of the nuclear power plant are abnormal, triggering the "primary early warning" of the intelligent inspection system, the system can automatically predict the risk change trend of the corresponding parameters. If there is a risk of exceeding the existing "alarm system" threshold within 30 minutes, the "secondary early warning" of the intelligent inspection system is triggered, issuing a dark-colored audible and visual alert to prompt the main control room operator to pay close attention, take timely maintenance measures, and warn of deterioration of the unit's condition, thereby improving the safety and economy of nuclear power plant operation.

[0111] Example 2:

[0112] This embodiment uses the example of a high flow rate of the main pump high-pressure cooler during intelligent auxiliary monitoring of the main control room of the process system operating status parameters under normal operating conditions in a nuclear power plant to describe the present invention in detail.

[0113] First, when the intelligent auxiliary monitoring system in the main control room issues a Level 1 warning for the unit's "main pump high-pressure cooler flow rate being too high," the operator, based on the criteria for identifying "false warnings," determines whether the Level 1 warning from the intelligent auxiliary monitoring system is a "real warning." The specific process is as follows:

[0114] 1) Determine if the unit is operating under the normal operating conditions applicable to the intelligent auxiliary monitoring system. Check if the current operating condition of the unit is within the normal operating conditions applicable to the system. If confirmed, the unit is operating under the normal operating conditions applicable to the system.

[0115] 2) Determine if the warning thresholds of the intelligent assisted inspection system are abnormal. Based on operational experience, the current system's warning thresholds show no obvious abnormalities.

[0116] 3) Determine if there are any data quality issues such as "missing data" or "outliers". After inspection, there are no invalid or erroneous data, missing data, or data acquisition noise issues. The current system's "Level 1 Warning" is confirmed as a "True Warning".

[0117] For confirmed "real warnings", the parameter trend prediction function of the intelligent auxiliary inspection system is activated to predict the development trend of the flow parameters of the main pump high-pressure cooler. According to the parameter trend prediction curve, there is a risk that the parameters will exceed the "alarm threshold" within 30 minutes, triggering the "level two warning" of the intelligent auxiliary inspection system.

[0118] At this point, activate the auxiliary decision-making information function of the intelligent auxiliary inspection system to view auxiliary decision-making information related to the flow parameters of the main pump high-pressure cooler, and formulate the corresponding operation and maintenance strategy based on the auxiliary decision-making information for the "main pump high-pressure cooler flow rate" warning. Details are as follows:

[0119] 1) Determine whether the "alarm threshold" expected to be exceeded by the warning parameter is the entry point for the unit's accident procedure. Based on the "high flow rate of the main pump high-pressure cooler" alarm response procedure in the warning parameter's auxiliary decision-making information, check whether the warning parameter is the entry point for the unit's accident procedure. It was confirmed that it is not the entry point for the accident procedure.

[0120] 2) Determine whether the "alarm threshold" expected to be exceeded by the warning parameter corresponds to the Limits on Operation (LCO) in the operational technical specifications. Based on the operational technical specifications, check whether the warning parameter corresponds to the LCO conditions in the operational technical specifications. It is confirmed that it is not an LCO condition in the operational technical specifications;

[0121] 3) Check whether exceeding the "alarm threshold" will trigger automatic interlocking actions. Based on the "high flow rate of main pump high-pressure cooler" alarm response procedure in the early warning parameter auxiliary decision information, check whether the early warning parameter will trigger automatic interlocking actions. It was confirmed that after the early warning parameter exceeds the "alarm threshold," it will trigger "WCC225VN / 265VN automatic shutdown, automatically isolating the equipment cooling water system (WCC)." At this time, first assess the impact of the relevant actions on the unit's operating status, make relevant preparations in advance, and take measures to prevent the parameters from deteriorating as much as possible.

[0122] Thirty minutes later, the warning parameters exceeded the "alarm threshold", triggering the unit's DCS alarm WCC031KA. The unit "WCC225VN / 265VN automatically shut down, automatically isolating the equipment's cooling water system". Based on the "high flow rate of the main pump high pressure cooler" alarm response procedure, relevant maintenance operations were performed.

[0123] Once the main control room operator confirms that the relevant maintenance activities have been completed, they click "Clear Warning" in the intelligent auxiliary inspection system.

[0124] Throughout the entire early warning response process of the intelligent auxiliary inspection system in the main control room, this invention can effectively identify "false alarms" issued by the system, avoiding false alarms. Simultaneously, by combining the auxiliary decision-making information corresponding to the early warning parameters in the system, a comprehensive operation and maintenance strategy can be formulated, accurately and efficiently assisting operators in their inspection work, improving their efficiency, reducing their workload, and ensuring the safe and stable operation of the nuclear power plant. This invention has a very significant effect on improving the safety and economy of nuclear power plant operation.

[0125] Example 3:

[0126] The intelligent auxiliary monitoring and early warning response device for nuclear power plant status parameters in this embodiment includes: a monitoring module, a primary early warning response module, and a secondary early warning response module.

[0127] The monitoring module is used to monitor the intelligent auxiliary inspection system and transmit the "Level 1 Early Warning" signal issued by the system indicating an abnormality in a certain status parameter of the unit to the Level 1 Early Warning Response module.

[0128] The Level 1 Early Warning Response Module is used to determine whether a Level 1 Early Warning signal indicating abnormal status parameters is a false alarm based on false alarm discrimination criteria. If so, it indicates that the intelligent assisted inspection system is malfunctioning or the operating condition is unsuitable. If not, it activates the parameter trend prediction function of the intelligent assisted inspection system to predict the development trend of the abnormal status parameters.

[0129] The monitoring module is also used to transmit the "Level 2 Early Warning" signal indicating abnormal status parameters issued by the intelligent assisted inspection system to the Level 2 Early Warning Response module.

[0130] The Level 2 Early Warning Response Module is used to activate the auxiliary decision-making information function of the intelligent auxiliary inspection system when a Level 2 Early Warning signal indicating abnormal status parameters is received. It generates corresponding preparatory work based on the category of consequences and risks, determines the category of consequences and risks, and executes the corresponding preparatory work.

[0131] In this embodiment,

[0132] The Level 1 early warning response module includes:

[0133] The first-level judgment module is used to determine whether the unit is operating under the normal operating conditions applicable to the intelligent auxiliary inspection system.

[0134] The first alert module is used to indicate that the "Level 1 Warning" is a false warning and the intelligent auxiliary inspection system is not applicable if the first-level judgment module determines that the unit is not operating under normal operating conditions applicable to the intelligent auxiliary inspection system.

[0135] The second-level judgment module is used to determine whether the warning thresholds of the intelligent auxiliary inspection system are abnormal when the unit is operating under the normal operating conditions applicable to the intelligent auxiliary inspection system, as determined by the first-level judgment module.

[0136] The second prompt module is used to indicate that the "Level 1 Warning" is a false warning and the intelligent assisted patrol system is unavailable due to the abnormal warning threshold when the Level 2 judgment module determines that the warning threshold of the intelligent assisted patrol system is abnormal.

[0137] The third-level judgment module is used to determine whether there are data quality issues when the second-level judgment module determines that the early warning threshold of the intelligent assisted inspection system is normal.

[0138] The third alert module is used to indicate that a "Level 1 Warning" is a false warning when the third-level judgment module determines that the intelligent assisted inspection system has data quality problems. This means the intelligent assisted inspection system is unavailable due to low data quality.

[0139] The first startup module is used to start the parameter trend prediction function of the intelligent auxiliary inspection system when the third-level judgment module determines that there is no data quality problem in the intelligent auxiliary inspection system, and to predict the development trend of abnormal status parameters.

[0140] In this embodiment,

[0141] The Level 2 early warning response module includes:

[0142] The second startup module is used to activate the auxiliary decision-making information function of the intelligent auxiliary inspection system 1 when a "level two early warning" signal indicating abnormal status parameters is received.

[0143] The generation module is used to generate corresponding preparatory work based on the information provided by the decision support information function module, according to the category of consequence risk.

[0144] The first and second level judgment modules are used to determine whether the "alarm threshold" that the abnormal state parameter is expected to exceed is the entry point of the unit's accident procedure.

[0145] The first execution module is used to perform preparatory work for entering the accident procedure when the first and second level judgment modules determine that the abnormal status parameters are expected to exceed the "alarm threshold" and that this is the entry point for the unit accident procedure.

[0146] The second-level judgment module is used to determine whether the "alarm threshold" expected to be exceeded by the abnormal state parameter corresponds to the operating restrictions in the operating technical specifications when the first-level judgment module determines that the "alarm threshold" is not the entry point of the unit accident procedure.

[0147] The second execution module is used to prepare corrective measures when the second and third level judgment modules determine that the abnormal status parameters are expected to exceed the "alarm threshold" corresponding to the operational limitations in the operating technical specifications.

[0148] The third-level judgment module is used to determine whether an automatic chain reaction will be triggered when the abnormal state parameter is expected to exceed the "alarm threshold" as determined by the second-level judgment module, which does not correspond to the operating restrictions in the operating technical specifications.

[0149] The third execution module is used to prepare for automatic chain actions when the third and second-level judgment modules determine that the abnormal state parameters are expected to exceed the "alarm threshold," which will trigger automatic chain actions.

[0150] The fourth execution module is used to perform corresponding preventive maintenance work based on the preventive maintenance plan in the auxiliary decision information function when the third and second level judgment modules determine that the abnormal status parameters expected to exceed the "alarm threshold" will not trigger automatic chain actions.

[0151] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for intelligent auxiliary patrol of state parameters of a nuclear power plant, characterized in that, Comprise: S1: monitoring the intelligent auxiliary patrol system, when it issues a "first level early warning" of a certain state parameter of the unit, judging whether the "first level early warning" of the intelligent auxiliary patrol system is a false early warning based on false early warning judgment conditions, if yes, prompting the intelligent auxiliary patrol system to be abnormal or the working condition to be not applicable, if no, turning to step S2, Specifically comprising: S1.1: judging whether the unit is running in a normal operation working condition applicable to the intelligent auxiliary patrol system, if yes, turning to step S1.2, if no, prompting the "first level early warning" to be a false early warning, and the intelligent auxiliary patrol system to be not applicable; S1.2: judging whether the early warning threshold of the intelligent auxiliary patrol system is abnormal, if no, turning to step S1.3, if yes, prompting the "first level early warning" to be a false early warning, and the intelligent auxiliary patrol system to be not applicable due to the abnormal early warning threshold; S1.3: judging whether there is a data quality problem, if yes, prompting the "first level early warning" to be a false early warning, and the intelligent auxiliary patrol system to be not applicable due to the low data quality, if no, turning to step S2; S2: starting the parameter trend prediction function of the intelligent auxiliary patrol system, developing trend prediction for the abnormal state parameter, monitoring the intelligent auxiliary patrol system, when it issues a "second level early warning" of the state parameter, turning to step S3, S3: starting the auxiliary decision information function of the intelligent auxiliary patrol system, generating corresponding preparation work according to the information provided by the auxiliary decision information function module, judging the category to which the consequence risk belongs, and executing the corresponding preparation work; judging the category to which the consequence risk belongs, and executing the corresponding preparation work, specifically comprising: S3.1: judging whether the "alarm threshold" that the abnormal state parameter is expected to break through is an entry of the unit accident procedure, if yes, executing the preparation work of entering the accident procedure, if no, turning to step S3.2, S3.2: judging whether the "alarm threshold" that the abnormal state parameter is expected to break through corresponds to the operation limit condition in the operation technical specification, if yes, executing the preparation work of the corrective measure, if no, turning to step S3.3, S3.3: judging whether the "alarm threshold" that the abnormal state parameter is expected to break through will trigger an automatic interlocking action, if yes, executing the preparation work of the automatic interlocking action, if no, turning to step S3.4, S3.4: executing the corresponding preventive maintenance work according to the preventive maintenance plan in the auxiliary decision information function.

2. The nuclear power plant state parameter intelligent auxiliary patrol early warning response method according to claim 1, characterized in that in the step S1.3, the data quality problem comprises invalid error data, data missing, and data acquisition noise.

3. The nuclear power plant state parameter intelligent auxiliary patrol early warning response method according to claim 1, characterized in that in the S1.2, after judging that the early warning threshold of the intelligent auxiliary patrol system is abnormal, further comprising: judging whether the model training data does not cover the applicable working condition, if yes, prompting the "first level early warning" to be a false early warning, if no, judging whether the system model is not trained sufficiently, if yes, prompting the "first level early warning" to be a false early warning, if no, turning to step S1.

2.

4. The nuclear power plant state parameter intelligent auxiliary patrol pre-warning response method according to claim 3, characterized in that, in S1.2, after prompting that the "first-level warning" is a false warning, further comprising: maintaining and updating the intelligent auxiliary patrol system, and after judging that the function of the updated system is restored to normal, putting the updated intelligent auxiliary patrol system online.

5. The nuclear power plant state parameter intelligent auxiliary patrol pre-warning response method according to claim 1, characterized in that, in S3.1, after performing the preparation work for entering the accident procedure, further comprising: monitoring whether the DCS system issues an alarm of the state parameter abnormality, if yes, performing the accident procedure according to the alarm procedure file corresponding to the state parameter abnormality; if no, continuing to perform the preparation work for entering the accident procedure; in S3.2, after performing the preparation work for the corrective measure, further comprising: monitoring whether the DCS system issues an alarm of the state parameter abnormality, if yes, performing the corresponding corrective measure according to the alarm procedure file corresponding to the state parameter abnormality; if no, continuing to perform the preparation work for the corrective measure; in S3.3, after performing the preparation work for the automatic interlock action, further comprising: monitoring whether the DCS system issues an alarm of the state parameter abnormality, if yes, performing the corresponding maintenance operation according to the alarm procedure file corresponding to the state parameter abnormality; if no, continuing to perform the preparation work for the automatic interlock action.

6. A nuclear power plant state parameter intelligent auxiliary patrol pre-warning response device, characterized in that, comprising: a monitoring module, a first-level warning response module and a second-level warning response module, the monitoring module is used for monitoring the intelligent auxiliary patrol system, and transmitting a "first-level warning" signal of a state parameter abnormality of a unit issued by the intelligent auxiliary patrol system to the first-level warning response module, the first-level warning response module is used for, when receiving the "first-level warning" signal of the state parameter abnormality, judging whether the "first-level warning" of the state parameter abnormality is a false warning based on a false warning judgment condition, if yes, prompting that the intelligent auxiliary patrol system is abnormal or the working condition is not applicable, and if no, starting a parameter trend prediction function of the intelligent auxiliary patrol system to predict the development trend of the abnormal state parameter, the monitoring module is further used for transmitting a "second-level warning" signal of the state parameter abnormality issued by the intelligent auxiliary patrol system to the second-level warning response module, the second-level warning response module is used for, when receiving the "second-level warning" signal of the state parameter abnormality, starting an auxiliary decision information function of the intelligent auxiliary patrol system, generating corresponding preparation work for the consequence risk category, judging the category to which the consequence risk belongs, and performing the corresponding preparation work; the first-level warning response module comprises: a first first-level judgment module used for judging whether the unit is running in a normal running working condition applicable to the intelligent auxiliary patrol system, a first prompting module used for, when the first first-level judgment module judges that the unit is not running in the normal running working condition applicable to the intelligent auxiliary patrol system, prompting that the "first-level warning" is a false warning and the intelligent auxiliary patrol system is not applicable, The second primary judgment module is configured to judge whether the early warning threshold of the intelligent auxiliary patrol system is abnormal when the first primary judgment module judges that the unit is running in the normal operation condition suitable for the intelligent auxiliary patrol system. The second prompt module is configured to prompt that the "primary early warning" is a false early warning when the second primary judgment module judges that the early warning threshold of the intelligent auxiliary patrol system is abnormal, and the intelligent auxiliary patrol system is unavailable due to the abnormal early warning threshold. The third primary judgment module is configured to judge whether there is a data quality problem when the second primary judgment module judges that the early warning threshold of the intelligent auxiliary patrol system is normal. The third prompt module is configured to prompt that the "primary early warning" is a false early warning when the third primary judgment module judges that there is a data quality problem in the intelligent auxiliary patrol system, and the intelligent auxiliary patrol system is unavailable due to the low data quality. The first start module is configured to start the parameter trend prediction function of the intelligent auxiliary patrol system to predict the development trend of the abnormal state parameter when the third primary judgment module judges that there is no data quality problem in the intelligent auxiliary patrol system.

7. The nuclear power plant state parameter intelligent auxiliary patrol early warning response device according to claim 6, wherein The secondary early warning response module comprises: The second start module is configured to start the auxiliary decision information function of the intelligent auxiliary patrol system when the "secondary early warning" signal of the abnormal state parameter is received. The generation module is configured to generate corresponding preparation work for the consequence risk category according to the information provided by the auxiliary decision information function module. The first secondary judgment module is configured to judge whether the "alarm threshold" that the abnormal state parameter is expected to break through is an entrance of the unit accident procedure. The first execution module is configured to perform the preparation work of entering the accident procedure when the first secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through is the entrance of the unit accident procedure. The second secondary judgment module is configured to judge whether the "alarm threshold" that the abnormal state parameter is expected to break through corresponds to the operation limitation condition in the operation technical specification when the first secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through is not the entrance of the unit accident procedure. The second execution module is configured to perform the preparation work of the corrective measure when the second secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through corresponds to the operation limitation condition in the operation technical specification. The third secondary judgment module is configured to judge whether the "alarm threshold" that the abnormal state parameter is expected to break through will trigger an automatic interlock action when the second secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through does not correspond to the operation limitation condition in the operation technical specification. The third execution module is configured to perform the preparation work of the automatic interlock action when the third secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through will trigger the automatic interlock action. The fourth execution module is configured to perform the corresponding preventive maintenance work according to the preventive maintenance plan in the auxiliary decision information function when the third secondary judgment module judges that the "alarm threshold" that the abnormal state parameter is expected to break through will not trigger the automatic interlock action.

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