Nuclear power unit risk prediction method, prediction system and evaluation system

Abstract

The application discloses a nuclear power unit risk prediction method, a prediction system and an evaluation system. The method comprises the following steps: acquiring a function of performance state of each device / component of a nuclear power unit changing with time; updating a risk prediction parameter value of a preset dynamic probabilistic safety analysis (Living PSA) model at a preset future time according to the function of performance state changing with time; judging whether the configuration of the nuclear power unit is changed at the preset future time, and if yes, updating the structure of the Living PSA model according to the changed configuration; and applying the risk prediction parameter value to the Living PSA model with the latest structure to obtain a risk prediction value of the nuclear power unit at the preset future time. The application can realize nuclear power unit risk prediction, improve the accuracy of the nuclear power unit risk prediction value, and predict the nuclear power unit risk at the future time in advance.

Description

Technical Field

[0001] This invention belongs to the field of nuclear power technology, specifically relating to a risk prediction method, prediction system, and assessment system for nuclear power units. Background Technology

[0002] Nuclear safety is the prerequisite and foundation for the development and application of nuclear energy. Probabilistic Safety Analysis (PSA) is a widely used nuclear safety analysis method both domestically and internationally, and it is incorporated into the requirements of the nuclear safety regulation HAF102. To monitor changes in the risks of nuclear power units caused by configuration changes during the operation phase, the industry has proposed the concept of Living PSA. During the unit operation phase, the PSA model is updated in a timely manner according to the actual configuration status of the unit to conduct real-time risk assessment, and corresponding risk monitors have been developed.

[0003] However, current risk monitors only consider changes in equipment operating status when performing configuration risk assessments, without taking into account the differences in equipment performance levels at different stages of its lifespan. These differences lead to variations in the reliability of equipment in performing relevant tasks during unit operation and accident mitigation, resulting in different actual risk levels for the unit. This makes the accuracy of risk monitoring insufficient.

[0004] Furthermore, existing technologies only assess the step risks of nuclear power units after changes in unit configuration, but cannot identify and predict the gradual risks caused by changes in equipment performance status over time. This may lead to missing the most favorable opportunity to carry out configuration management in advance and avoid entering a high-risk situation, which is not conducive to improving the safety and availability of nuclear power units. Summary of the Invention

[0005] 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, prediction system and evaluation system for predicting nuclear power unit risks, which can realize the prediction of nuclear power unit risks, improve the accuracy of nuclear power unit risk prediction values ​​and predict nuclear power unit risks in advance.

[0006] In a first aspect, the present invention provides a method for predicting the risks of nuclear power units, comprising:

[0007] A function to obtain the performance status of each piece of equipment / component of a nuclear power unit as a function of time;

[0008] The risk prediction parameter values ​​of the preset dynamic probabilistic security analysis (Living PSA) model at a preset future time are updated based on the function of the performance state changing over time.

[0009] Determine whether the configuration of the nuclear power unit has changed at the preset future time. If so, update the structure of the Living PSA model according to the changed configuration.

[0010] The risk prediction parameter values ​​are applied to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time.

[0011] Preferably, the function for obtaining the performance status of each piece of equipment / component of the nuclear power unit over time specifically includes:

[0012] Obtain the operating parameters of each piece of equipment / component of the nuclear power unit at the current moment;

[0013] The operating parameters are compared with the full lifecycle data of each device / component to obtain the current lifecycle stage of each device / component and the degree of change in performance status over time.

[0014] Based on the current lifespan stage and the degree of performance change over time, a function is used to predict the performance change of each device / component over a preset future time period starting from the current moment.

[0015] Preferably, the risk prediction parameter values ​​specifically include: the basic event probability of the fault tree model in the Living PSA model, the frequency of system failure initiation events and the frequency of equipment / component failure initiation events in the event tree model;

[0016] The step of updating the risk prediction parameter values ​​of the preset dynamic probabilistic security analysis (LivingPSA) model at a preset future time based on the function of the performance state changing over time specifically includes:

[0017] Based on the function of the performance state changing over time, the basic event probability Q of the fault tree model at the preset future time is obtained. (t) ;

[0018] Using the basic event probability Q (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure initiation events of the event tree model at the preset future time.

[0019] Based on the function of the performance state changing over time, obtain the frequency of device / component failure initiation events fr at the preset future time in the event tree model. IEi .

[0020] Preferably, the function of performance status changing over time is specifically a distribution function of the remaining useful life of the device / component.

[0021] Preferably, the basic event probability Q of the fault tree model at a preset future time is obtained based on the function of the performance state changing over time. (t) Specifically:

[0022] The probability Q of the basic event is obtained by calculating the following formula. (t) :

[0023]

[0024] Where: t is the preset future time; λ (s) The failure rate of the device / component corresponding to the basic event; T m The required time for the corresponding equipment / components to be put into operation to ensure safe system operation or mitigate accidents; θ (s) and θ (u) These are the expressions for the remaining useful life distribution functions of the corresponding devices / components, with s and u as time variables; s is the time variable from t to t+T. m The distribution function θ of the remaining useful life at any given time (s) The time variable used for integration; u is the distribution function θ of the remaining useful life from time 0 to time s. (u) The time variable used for integration.

[0025] Preferably, the frequency of device / component failure initiation events at a preset future time is obtained from the function of the performance state changing over time. IEi Specifically:

[0026] The frequency of the initiation event of the device / component failure type is obtained by calculating the following formula: IEi :

[0027]

[0028] Where: λ (t) The failure rate of the equipment / component corresponding to the initiation event of the equipment / component failure type; T is the average annual operating time of the corresponding equipment / component under a certain operating condition; θ (t) and θ (s) These are the expressions for the remaining useful life distribution function of the corresponding equipment / component, with t and s as time variables; t is the expression for the remaining useful life distribution function θ from time 0 to time T. (t) The time variable used for integration; s is the distribution function θ of the remaining useful life from time 0 to time t. (t) The time variable used for integration.

[0029] Preferably, determining whether the configuration of the nuclear power unit has changed at the preset future time, and if so, updating the structure of the Living PSA model according to the changed configuration, specifically includes:

[0030] Receive the configuration of the nuclear power unit automatically monitored by the DCS system or manually input through the human-machine interface. The configuration includes the state of the nuclear power unit's system / equipment at a preset future time, and determine whether the state has changed.

[0031] If so, update the event logic values ​​of the system / device in the Living PSA model to obtain the Living PSA model with the latest structure.

[0032] Preferably, the risk prediction value for the preset future time specifically includes at least one of the following:

[0033] The minimum cut set of the event tree / fault tree in the Living PSA model, the risk indicators of the nuclear power unit, the system failure probability, and the risk importance of the system / equipment.

[0034] Preferably, after applying the risk prediction parameter values ​​to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time, the method further includes:

[0035] Multiple preset future moments are selected within a preset future time period, and the risk prediction values ​​of the multiple preset future moments are obtained and combined to obtain the risk prediction result of the nuclear power unit within the preset future time period.

[0036] Preferably, obtaining and combining multiple preset future time-time risk prediction values ​​to obtain a risk prediction result for the nuclear power unit within the preset time period specifically includes:

[0037] Obtain predicted risk indicators for the nuclear power unit at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result for the nuclear power unit within the preset future time period; and / or,

[0038] The predicted risk importance of the key systems / equipment of the nuclear power unit at multiple preset future time points is obtained. A predicted risk list is established based on the relationship between the predicted risk importance and time, and the predicted risk list is visualized as the risk prediction result of the nuclear power unit within the preset future time period.

[0039] In a second aspect, the present invention provides a nuclear power unit risk prediction system, comprising:

[0040] The acquisition module is a function used to acquire the performance status of each device / component of a nuclear power unit over time.

[0041] The first update module, connected to the acquisition module, is used to update the risk prediction parameter values ​​of the preset dynamic probabilistic security analysis (Living PSA) model at a preset future time according to the function of the performance status changing over time.

[0042] The second update module, connected to the first update module, is used to determine whether the configuration of the nuclear power unit has changed at the preset future time. If so, the structure of the Living PSA model is updated according to the changed configuration.

[0043] A risk prediction module, connected to the second update module, is used to apply the risk prediction parameter values ​​to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time.

[0044] Preferably, the risk prediction parameter values ​​specifically include: the basic event probability of the fault tree model in the Living PSA model, the frequency of system failure initiation events and the frequency of equipment / component failure initiation events in the event tree model;

[0045] The first update module specifically includes:

[0046] The first parameter update unit is used to obtain the basic event probability Q of the fault tree model at a preset future time based on the function of the performance state changing over time. (t) ;

[0047] The second parameter update unit is used to utilize the basic event probability Q. (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure initiation events of the event tree model at the preset future time.

[0048] The third parameter update unit is used to obtain the frequency of device / component failure initiation events at a preset future time based on the function of the performance state changing over time. IEi .

[0049] Preferably, the system further includes:

[0050] The result combination module, connected to the risk prediction module, is used to select multiple preset future times within a preset future time period, obtain the risk prediction values ​​of the multiple preset future times, and combine them to obtain the risk prediction result of the nuclear power unit within the preset future time period.

[0051] Preferably, the result combining module specifically includes:

[0052] A risk prediction curve unit is used to obtain predicted risk indicators for the nuclear power unit at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result for the nuclear power unit within the preset future time period; and / or,

[0053] The predicted risk list unit is used to obtain the predicted risk importance of the key systems / equipment of the nuclear power unit at multiple preset future times, establish a predicted risk list based on the relationship between the predicted risk importance and time, and visualize the predicted risk list as the risk prediction result of the nuclear power unit in the preset future time period.

[0054] A third aspect of the present invention provides a nuclear power unit risk assessment system, comprising:

[0055] The risk monitoring function module is used to execute methods for risk monitoring based on the current performance status of each piece of equipment / component of the nuclear power unit; and,

[0056] The risk prediction function module is used to execute the nuclear power unit risk prediction method described above.

[0057] The nuclear power unit risk prediction method, prediction system, and assessment system provided by this invention predict the future progressive risks of nuclear power units based on the function of the performance status of nuclear power unit equipment / components changing over time. Building upon existing nuclear power unit risk assessment technologies, this invention adds consideration to risk factors arising from changes in equipment performance status over time. It can accurately predict the future risk level of nuclear power units based on changes in equipment performance, identify key weaknesses, guide nuclear power plant operation and maintenance personnel to conduct targeted configuration risk management, facilitate nuclear power unit risk prevention, improve nuclear power unit operational safety, increase nuclear power unit availability, and ultimately improve its economic efficiency. In short, it enhances both the safety and economic viability of nuclear power units. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the nuclear power unit risk prediction method in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of a nuclear power plant structure in an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of a nuclear power unit risk assessment method according to another embodiment of the present invention;

[0061] Figure 4 This is a typical remaining useful life distribution curve in another embodiment of the present invention;

[0062] Figure 5 This is a schematic diagram illustrating the correspondence between the risk assessment time domain and the operation time domain in another embodiment of the present invention;

[0063] Figure 6 This is a fault tree model diagram of TFA001PO in the non-testing stage in another embodiment of the present invention;

[0064] Figure 7 This is a fault tree model diagram of the TFA001PO test phase in another embodiment of the present invention;

[0065] Figure 8 This is a CDF risk curve diagram in another embodiment of the present invention;

[0066] Figure 9 This is a schematic diagram of the nuclear power unit risk prediction system in an embodiment of the present invention;

[0067] Figure 10 This is a schematic diagram of the nuclear power unit risk assessment system in an embodiment of the present invention. Detailed Implementation

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] In the description of this invention, each unit or module may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure; the units or modules may be implemented by software or by hardware, for example, the units or modules may be located in a processor.

[0073] In the description of this invention, unless otherwise specified, the functions and steps marked in the flowcharts and block diagrams of this invention may occur in a different order than that marked in the accompanying drawings.

[0074] To facilitate understanding of this invention, the existing risk monitors for nuclear power plants will first be described. According to the "Technical Policy (Trial) for Configuration Risk Management of Nuclear Power Plants" issued by the National Nuclear Safety Administration of my country on December 31, 2019, nuclear power plant operators conduct configuration risk management for nuclear power plant operation and maintenance activities to compensate for the shortcomings of current technical specifications in addressing the insufficient consideration of the complexity and diversity of nuclear power plant configuration combinations, ensuring that the safety level of nuclear power plants is maintained or even improved. Specifically, the Living PSA method and risk monitors based on this method are specified as the basic methods and tools for configuration risk management.

[0075] The Living PSA method is based on the PSA method. The PSA method uses event tree and fault tree models to conduct a comprehensive and systematic analysis of the safety risks that may arise during the operation of nuclear power plant units, assess the risk level of the unit, identify risk sources, development paths and consequences, so as to guide nuclear power plant designers or operators to take targeted measures to eliminate or reduce risks.

[0076] To ensure the effectiveness of risk assessment, the model used in PSA (Power Scheme Allocation) should be matched to the actual configuration of the nuclear power unit. However, throughout the entire lifespan of a nuclear power unit, from design to decommissioning, configuration changes are inevitable, such as system upgrades and equipment maintenance. To monitor risk changes caused by configuration changes during operation, the PSA model needs to be updated promptly based on the actual configuration status for real-time risk assessment—this is known as Living PSA. Risk monitoring software based on Living PSA has been developed as a risk monitor for nuclear power plants.

[0077] However, due to limitations in past monitoring technologies, existing nuclear power plant risk monitors only consider changes in equipment operating status, such as equipment changing from "operating" to "out of service" or entering "maintenance unavailable," when conducting configuration risk assessments. They fail to address performance differences across different stages of equipment's lifespan. These differences lead to varying reliability in performing related tasks during operation and accident mitigation, resulting in inconsistent actual risk levels for the nuclear power unit and insufficient accuracy in risk monitoring. Furthermore, existing technologies only conduct risk assessments after unit configuration changes, failing to identify and predict the gradual changes in risk caused by changes in equipment performance over time. This makes risk monitoring unable to anticipate potential risks in the future.

[0078] This has at least the following adverse effects on configuration management:

[0079] (1) Because the assessment results are highly uncertain, there is a lack of confidence in risk management, which limits the scope, depth and effectiveness of the technology in nuclear power plants;

[0080] (2) Because the performance differences of the equipment are ignored, the risks may be underestimated and key risk factors may not be accurately identified, which may lead to the risk of non-conservative decision-making.

[0081] (3) Because future risks cannot be predicted, the best opportunity to carry out configuration management in advance to avoid entering a high-risk state may be missed. Once a high-risk state is entered, the management measures that can be taken are also relatively limited, which restricts the improvement of unit availability.

[0082] In recent years, with the rapid development of information technology, computer technology and artificial intelligence, the advanced monitoring, diagnosis and prediction capabilities of industrial systems and equipment have been significantly improved, making it feasible to monitor and predict the performance level of equipment in real time during the operation of nuclear power units, and providing a technical foundation for solving the above problems.

[0083] Example 1:

[0084] like Figure 1 As shown, Embodiment 1 of the present invention provides a method for predicting the risks of nuclear power units.

[0085] Specifically, in this embodiment, a nuclear power unit risk assessment method is provided, which is applied to, for example... Figure 2 The nuclear power unit risk assessment system 20 shown includes methods for monitoring nuclear power unit risks and such as... Figure 1 The nuclear power unit risk prediction method shown is used to predict the risk of nuclear power units. Figure 2The nuclear power unit 10 in the nuclear power plant 100 shown undergoes current risk monitoring and future risk prediction during its operation phase, and this is applied to the development of corresponding software, forming a nuclear power unit risk assessment system 20 for nuclear power unit 10. More specifically, this method is a risk monitoring and prediction method for nuclear power unit 10 based on the relationship between the performance of each piece of equipment in nuclear power unit 10 and the changes over time. Risk monitoring assesses the current risk, at which point the performance of the equipment / components can be measured; risk prediction assesses the future risk, which needs to consider changes in the performance of the equipment / components over future time, and this is the focus of this invention. The developed software - nuclear power unit risk assessment system 20 - is planned to be named: Performance-Based Risk Monitoring and Prediction System PB-RMP. It improves upon the existing risk monitors used in nuclear power plant 100 by introducing a function of the performance of each piece of equipment in nuclear power unit 10 changing over time, in order to accurately assess the changes in the risk level of nuclear power unit 10 from the current moment to a future period. By applying the proposed PB-RMP system, risk monitoring and prediction can be carried out in a timely manner when changes occur in unit configuration and equipment / component performance levels, thereby improving the accuracy and predictability of risk assessment. This will guide operation and maintenance personnel to carry out configuration risk management as early and in a targeted manner, ensuring the safe and economical operation of nuclear power units 10 and improving the safety and economy of nuclear power plants 100.

[0086] In addition, such as Figure 1 The nuclear power unit risk prediction method shown can also be developed independently, such as... Figure 9 The nuclear power unit risk prediction system 202 shown is only used to predict the future risks of nuclear power unit 10.

[0087] In a more specific embodiment, the Fuqing Nuclear Power Hualong Unit will be used as the object, and its power operation condition level 1 PSA model will be used as the basis to conduct risk monitoring and prediction under a set scenario, so as to demonstrate the application effect of the method of the present invention in more detail.

[0088] S1 is a function that obtains the performance status of each of the 10 devices / components of the nuclear power unit over time.

[0089] Specifically in this embodiment, such as Figure 3As shown, in order to accurately assess the risks of nuclear power unit 10 at the current moment and over a future period, it is first necessary to obtain the function of the performance status of the equipment / components of nuclear power unit 10 involved in this risk assessment as a function of time. Here, only the case of equipment performance degradation over time is considered. Therefore, step S100 is executed: obtaining the performance degradation function, which is used as the input for subsequent updates of the Living PSA model parameters. The risk assessment of this invention is continuously and dynamically implemented throughout the entire operation of the unit. Since the longer the equipment / component operates, the worse its performance and the corresponding risk increase, the impact of equipment / component performance degradation over time is considered during the risk assessment. This allows for a progressive risk assessment result that gradually increases over time, enabling more accurate monitoring of current risks and prediction of future risks.

[0090] In an optional embodiment, the function for obtaining the performance status of each piece of equipment / component of the nuclear power unit 10 over time specifically includes:

[0091] Obtain the operating parameters of each piece of equipment / component of the nuclear power unit 10 at the current moment;

[0092] The operating parameters are compared with the full lifecycle data of each device / component to obtain the current lifecycle stage and performance status of each device / component.

[0093] Based on the current lifespan stage and performance status, a function is used to predict the performance status of each device / component over a preset future time period starting from the current moment.

[0094] Specifically in this embodiment, such as Figure 3 As shown, in order to obtain the performance degradation function of each piece of equipment / component of the nuclear power unit 10, sensors are first installed on the necessary equipment / components of the nuclear power unit 10. The sensors perform step S101: measuring operating parameters and sending the real-time operating parameters to the nuclear power unit risk assessment system 20. After receiving the operating parameters of the corresponding equipment / component, the nuclear power unit risk assessment system 20 performs step S102: evaluating performance. Specifically, it can compare and determine the current life stage and performance status of the equipment / component as reflected by the operating parameters based on the full life-cycle data given by the equipment / component manufacturer or the nuclear power plant 100 based on its own experience. Then, based on the current life stage and performance status, the performance degradation function of the equipment / component in the preset future time period starting from the current moment is obtained.

[0095] Since predicting the future performance status of equipment / components over time involves the specialized technical field of equipment fault diagnosis, the industry has researched and developed various model methods for monitoring and predicting equipment performance. In implementation, the appropriate model method can be selected according to the specific equipment object. By drawing on equipment performance monitoring and prediction methods from other technical fields and applying them to this field, it is possible to obtain the function of the performance status of each of the 10 equipment / components of the nuclear power unit changing over time. This invention does not need to limit the specific method for obtaining the function of the performance status changing over time.

[0096] In an optional embodiment, the function of performance state over time is specifically a distribution function of the remaining useful life of the device / component.

[0097] Specifically in this embodiment, such as Figure 3 As shown, the performance degradation function of the device / component is predicted for a preset future time period starting from the current moment. This is specifically achieved by executing step S103: predicting the remaining service life (RUL). RUL can be described in various ways and can be converted between each other. Figure 4 This diagram illustrates a typical remaining useful life distribution curve, where the horizontal axis represents time t, and the vertical axis represents the predicted value θ of the remaining useful life distribution function expressed in terms of reliability. (t) .

[0098] In a more specific embodiment, for the Fuqing Nuclear Power Plant Hualong Unit, the risk of Unit 10 within a preset time period from the current moment to the next 150 hours is first assessed. During this process, to ensure equipment reliability, according to the requirements of the nuclear power plant technical specifications, the auxiliary feedwater electric pump TFA001PO needs to be tested periodically. It is assumed that, according to the production plan, the auxiliary feedwater electric pump TFA001PO will be tested between the 30th and 50th hours. Simultaneously, the performance degradation of the equipment cooling water pump WCC001PO and the charging pump RCV001PO within the preset time period is considered. Since the upgrade and update of the basic reliability database is part of the maintenance work of the Living PSA model during unit refueling shutdowns, and risk monitoring and prediction during the operation phase do not involve such changes, they are not considered in this example. Assuming that WCC001PO and RCV001PO follow the same degradation law, this does not affect the discussion of the embodiments of the present invention. Based on existing publicly available data, the remaining useful life of WCC001PO and RCV001PO is predicted. The remaining useful life distribution is described by the probability of the equipment failing per unit time at a preset future time. The results are shown in columns 1 and 2 of Table 1. In the present invention, these results are only used as input for risk assessment. They can be obtained by selecting an appropriate model according to the equipment fault diagnosis technology. Therefore, the prediction process will not be discussed here.

[0099] S2. Update the risk prediction parameter value of the preset dynamic probabilistic security analysis (LivingPSA) model at the preset future time according to the function of the performance state changing over time.

[0100] Specifically in this embodiment, such as Figure 3 As shown, after obtaining the performance degradation function, the nuclear power unit risk assessment system 20 executes step S200: updating the Living PSA model parameters. This is the core function of the present invention. Existing technologies only consider the performance of equipment / components at the current moment, without considering the impact of performance degradation over time. Their prediction of future risks is still based on current performance, which leads to their risk estimates being lower than the actual level.

[0101] In a more specific embodiment, a typical example of the prediction results of the existing method is shown in Figure 8 As can be seen from the figure, the curve shown by the existing method only considers the step risk of nuclear power unit 10 during configuration changes, and cannot obtain the gradual risk shown by the method proposed in this invention. To obtain... Figure 8 The incremental risk proposed in the method requires obtaining the time-varying risk prediction parameter values ​​of the Living PSA model based on the function of performance state changing over time. That is, based on the obtained function of performance state changing over time, the risk prediction parameter values ​​of the corresponding equipment / component at the time when the risk needs to be predicted within a preset future time period are updated. The risk prediction time is a number of time points within a preset future time period set by the assessor according to the prediction needs. Each risk prediction parameter value will reflect the characteristics of the risk of the corresponding equipment / component changing over time.

[0102] In an optional embodiment, the risk prediction parameter values ​​specifically include: the basic event probability of the fault tree model in the Living PSA model, the frequency of system failure-type initiating events and the frequency of equipment / component failure-type initiating events in the event tree model;

[0103] The step of updating the risk prediction parameter value of the preset dynamic probabilistic security analysis (LivingPSA) model at the preset future time based on the function of the performance state changing over time specifically includes:

[0104] Based on the function of the performance state changing over time, the basic event probability Q of the fault tree model at the preset future time is obtained. (t) ;

[0105] Using the basic event probability Q (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure initiation events of the event tree model at the preset future time.

[0106] Based on the function of the performance state changing over time, obtain the frequency of device / component failure initiation events fr at the preset future time in the event tree model. IEi .

[0107] Specifically in this embodiment, such as Figure 3 As shown, the parameters used for quantitative analysis using the Living PSA model mainly include the basic event probabilities of the fault tree model and the initiating event frequency of the event tree model. Therefore, updating the parameters of the Living PSA model mainly includes steps S201: updating the basic event probabilities of the fault tree and S202: updating the initiating event frequency of the event tree. Depending on the object triggering the initiating event, initiating events can be divided into two categories: equipment / component failures, such as pipeline rupture accidents; and system failures, such as heat sink loss accidents. The frequency of initiating events in the system failure category needs to be calculated using system analysis methods such as fault tree analysis. This is a known method for the PSA model. This invention simply uses the basic event probability Q when obtaining the frequency of initiating events in the system failure category using fault tree analysis. (t) The impact of changes in the performance status of equipment / components over time has been taken into account.

[0108] In an optional embodiment, the basic event probability Q of the fault tree model at a preset future time is obtained based on a function of the performance state changing over time. (t) Specifically:

[0109] The probability Q of the basic event is obtained by calculating the following formula. (t) :

[0110]

[0111] Where: t is the preset future time; λ (s) The failure rate of the device / component corresponding to the basic event; T m The required time for the corresponding equipment / components to be put into operation to ensure safe system operation or mitigate accidents; θ (s) and θ (u) These are the expressions for the remaining useful life distribution functions of the corresponding devices / components, with s and u as time variables; s is the time variable from t to t+T. m The distribution function θ of the remaining useful life at any given time (s) The time variable used for integration; u is the distribution function θ of the remaining useful life from time 0 to time s. (u) The time variable used for integration.

[0112] In an optional embodiment, the frequency of device / component failure initiation events at a preset future time is obtained from the function of the performance state changing over time. IEi Specifically:

[0113] The frequency of the initiation event of the device / component failure type is obtained by calculating the following formula: IEi :

[0114]

[0115] Where: λ (t) The failure rate of the equipment / component corresponding to the initiation event of the equipment / component failure type; T is the average annual operating time of the corresponding equipment / component under a certain operating condition; θ (t) and θ (s) These are the expressions for the remaining useful life distribution function of the corresponding equipment / component, with t and s as time variables; t is the expression for the remaining useful life distribution function θ from time 0 to time T. (t) The time variable used for integration; s is the distribution function θ of the remaining useful life from time 0 to time t. (t) The time variable used for integration.

[0116] Specifically in this embodiment, Figure 5 The solid line represents the time frame for risk assessment, with the current time as point 0, monitoring the risk of nuclear power unit 10 at the current time and predicting future times. The dashed line represents the operating time domain of equipment / components, with point 01 as the time when it last completed maintenance and was put into operation. The solid line represents the assessment time domain for risk assessment of nuclear power unit 10, with point 02 as the zero point of risk assessment, which is the current time. Obviously, the zero point of equipment / component operation is earlier than the zero point of risk assessment because risk assessment is continuously and dynamically implemented throughout the entire operation of the unit, while equipment / components may have been put into operation when the unit was started. The cumulative impact of this operation will be reflected in performance changes. That is, the cumulative operating time T0 from the last maintenance to the current time will affect the RUL distribution function θ. (t) This, in turn, affects the probability Q of the basic event. (t) and the frequency of the initiating event fr IEi In the diagram, t represents the moment when the risk is to be assessed, which can be any preset future moment within a predetermined future time period. t = 0 represents monitoring the instantaneous risk at the current moment, i.e., risk monitoring; t > 0 represents predicting the instantaneous risk at a future moment, i.e., risk prediction. m To ensure the safe operation of nuclear power units or mitigate accidents, the required operational time for equipment / components is necessary. The frequency of initiating events related to equipment / component failures is also considered. IEiThe risk level of nuclear power unit 10 is obtained by integrating the annual average operating time of the equipment / component under a certain operating condition. This is because risk assessment based on PSA needs to be modeled and calculated separately for different operating conditions. Finally, the risk level is obtained by weighting the proportion of the duration of different operating conditions.

[0117] In a more specific embodiment, for updating the parameters of the Living PSA model, it is first necessary to analyze the scope of its impact, i.e., whether it affects the initiating events of the event tree and the basic events of the fault tree. Since the initiating events are a specific accident list derived from system analysis of the nuclear power plant, the change in this example is assumed not to cause the accidents in the list to occur or change their frequency. Here, it only affects the failure probability of the basic events in the fault tree corresponding to the corresponding equipment. Therefore, it is necessary to monitor and predict the probability of the basic events in the fault tree within 0-150 hours. Based on the basic event probability Q... (t) The calculation formula can be used to calculate the probability of the model events corresponding to the degraded devices WCC001PO and RCV001PO occurring within the next 150 hours, that is, the probability of the devices failing to perform the accident mitigation task. The calculation results are shown in column 3 of Table 1. This example is only used to demonstrate the technical method and effect of the present invention, so the calculation has been simplified. That is, it is assumed that WCC001PO and RCV001PO follow the same degradation law. Therefore, the obtained RUL distribution and the parameter law of the Living PSA model are the same.

[0118] Table 1: Prediction Results of RUL Distribution and Living PSA Model Parameters

[0119]

[0120]

[0121] S3. Determine whether the configuration of the nuclear power unit has changed at the preset future time. If so, update the structure of the Living PSA model according to the changed configuration.

[0122] Specifically in this embodiment, such as Figure 3 As shown, step S300: updating the Living PSA model structure has a starting condition, namely step S303: determining whether the unit configuration has changed. Unit configuration changes include situations such as equipment failure, switching between operating and standby columns, etc. If the unit configuration has changed, the Living PSA model structure needs to be updated; otherwise, the model structure does not need to be updated, and the process can proceed directly to the subsequent risk assessment steps.

[0123] In an optional embodiment, determining whether the configuration of the nuclear power unit has changed at the preset future time, and if so, updating the structure of the Living PSA model according to the changed configuration, specifically includes:

[0124] Receive the configuration of the nuclear power unit automatically monitored by the DCS system or manually input through the human-machine interface. The configuration includes the state of the nuclear power unit's system / equipment at a preset future time, and determine whether the state has changed.

[0125] If so, update the event logic values ​​of the system / device in the Living PSA model to obtain the Living PSA model with the latest structure.

[0126] Specifically in this embodiment, such as Figure 3 As shown, the criteria for judging unit configuration changes include: step S301: DCS system monitoring, or S302: human-machine interface input, based on the system / equipment status automatically monitored by the DCS system or manually input by the user through the human-machine interface to determine whether the unit configuration has changed at the time t when the risk is to be assessed.

[0127] In a more specific embodiment, such as Figure 6 and 7 As shown, the Living PSA model structure changes during the TFA001PO testing phase, with the changed objects marked in red. In the non-testing and testing phases of TFA001PO, the logical values ​​for this event are "Normal" and "True," respectively, indicating that the event "has a certain probability of being unavailable" and "is definitely unavailable." This "certain probability" is affected by equipment performance. In this example, since other equipment has not experienced any changes in status or performance, they remain unchanged. The logical values ​​of other parts are determined by the status of the corresponding equipment: "Normal" if the equipment is running, and "True" if the equipment has failed. These are rules already fixed in current PSA technology. Therefore, for the Living PSA model structure update, it only needs to be considered when TFA001PO is unavailable during the 30-50 hour test. The update method is to change the logical value of the fault event corresponding to TFA001PO in the model to "True," meaning the probability of this event is set to 1, the equipment is definitely unavailable, and correspondingly, the overall risk level of nuclear power unit 10 will increase.

[0128] S4. Apply the risk prediction parameter value to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time.

[0129] In an optional embodiment, the preset risk prediction value for future moments specifically includes at least one of the following:

[0130] The minimum cut set of the event tree / fault tree in the Living PSA model, the risk indicators of the nuclear power unit, the system failure probability, and the risk importance of the system / equipment.

[0131] Specifically in this embodiment, such as Figure 3 As shown, after updating the Living PSA model and its parameters, step S400 is executed: Living PSA model risk assessment. This part only changes the input model parameters; the results that can be calculated and analyzed are the same as those of existing risk monitors, including: minimum cut set solution for event trees / fault trees, calculation of nuclear power unit 100 risk indicators, system failure probability prediction, and risk importance analysis of systems / equipment, etc. Among these, the nuclear power unit 100 risk indicators include: core damage frequency (CDF) and radioactive release frequency (LERF), etc.

[0132] In an optional embodiment, after applying the risk prediction parameter value to the Living PSA model of the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time, the method further includes:

[0133] Multiple preset future times are selected within a preset future time period, and the risk prediction values ​​for these multiple preset future times are obtained and combined to obtain the risk prediction result for the nuclear power unit within the preset future time period. Specifically, in this embodiment, as... Figure 3 As shown, after completing the risk assessment for the preset time period, step S500 is executed: outputting risk information so that the maintenance personnel of nuclear power plant 100 can perform configuration risk management for nuclear power unit 10 based on the risk analysis results. To facilitate the observation of risk analysis results by maintenance personnel, the results obtained from the analysis and calculation are further processed and visualized when the risk information is output and displayed. Specifically, this includes: visualization of unit risk information, such as risk curves, lists of risk-important systems / equipment, etc.; calculation and display of configuration risk management indicators, such as cumulative risk increments, allowable configuration time, etc.; and providing human-computer interaction functions such as risk information query and risk report printing. These functions can also be achieved by existing risk monitors; the only difference in the magnitude and trend of risk reflected by the results obtained in this invention is that they are different.

[0134] In an optional embodiment, obtaining and combining multiple preset future time-time risk prediction values ​​to obtain a risk prediction result for the nuclear power unit within the preset future time period specifically includes:

[0135] Obtain predicted risk indicators for the nuclear power unit at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result for the nuclear power unit within the preset future time period; and / or,

[0136] The predicted risk importance of the key systems / equipment of the nuclear power unit at multiple preset future time points is obtained. A predicted risk list is established based on the relationship between the predicted risk importance and time, and the predicted risk list is visualized as the risk prediction result of the nuclear power unit within the preset future time period.

[0137] In a more specific embodiment, since there is currently no software tool available to implement the method described in this invention, the widely used PSA modeling and analysis software Risk Spectrum is used as the analysis and calculation tool. Multi-point calculations are performed by manually changing the model parameters multiple times. After the Living PSA model is updated at each timet where the risk is to be assessed, the Risk Spectrum software is used to perform risk analysis calculations. The final results include:

[0138] like Figure 8 The CDF risk curve shown in the figure illustrates that, in the method proposed in this invention, the instantaneous risk of the unit continuously increases with equipment aging. At hour 30, due to the unavailability of the TFA001PO test, the unit risk increases dramatically. At hour 50, the test ends, and the unit returns to its initial state, resulting in a dramatic decrease in risk. However, due to continued equipment performance degradation, the unit's risk level is higher than before the test. In contrast, existing methods only consider the step risk change when the test becomes unavailable, without taking into account equipment aging. This leads to overly optimistic unit risk assessment results and an inability to predict high-risk states of the unit in advance.

[0139] Table 2 shows the Fussel-Vesely Importance (FV) of the failure events of WCC001PO and RCV001PO. As can be seen from the table, the importance of the relevant failure events changes with the performance of the equipment. Although the degradation patterns are the same for both, the importance of the RCV001PO failure increases rapidly with performance degradation, and is significantly higher than that of WCC001PO. This is because the redundancy of the RCV system is low. Therefore, although both are close to failure, the maintenance of RCV001PO should be given priority in the maintenance plan. It can be seen that even assuming that the equipment performance degradation patterns are exactly the same, their impact on risks and risk management measures are different.

[0140] Table 2. Ranking of FV Importance

[0141]

[0142] Based on the above results analysis, it is shown that the system solution proposed in this invention is feasible. Compared with the existing solutions, it has the following advantages: (1) It considers the impact of equipment performance changes on risk in risk assessment, providing more realistic risk assessment results; (2) It can assess the changes in equipment importance as its performance degrades, and propose priority suggestions for equipment maintenance from the unit level, making operation and maintenance management more targeted and resource allocation more optimized; (3) By predicting equipment failures and potential high risks in advance, it can avoid unplanned outages caused by sudden failures and provide a longer time window for operation and maintenance management.

[0143] Example 2:

[0144] like Figure 9 As shown, Embodiment 2 of the present invention provides a nuclear power unit risk prediction system 202, the system comprising:

[0145] Module 1 is used to obtain a function that changes the performance status of each piece of equipment / component of the nuclear power unit over time.

[0146] The first update module 2, connected to the acquisition module 1, is used to update the risk prediction parameter values ​​of the preset dynamic probabilistic security analysis (Living PSA) model at a preset future time according to the function of the performance status changing over time.

[0147] The second update module 3, connected to the first update module 2, is used to determine whether the configuration of the nuclear power unit 10 has changed at the preset future time. If so, the structure of the Living PSA model is updated according to the changed configuration.

[0148] The risk prediction module 4, connected to the second update module 3, is used to apply the risk prediction parameter value to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit 10 at the preset future time.

[0149] In an optional embodiment, the acquisition module 1 specifically includes:

[0150] The acquisition unit is used to acquire the operating parameters of each piece of equipment / component of the nuclear power unit 10 at the current moment;

[0151] An evaluation unit, connected to the acquisition unit, is used to compare the operating parameters with the full lifecycle data of each device / component to obtain the current lifecycle stage and performance status of each device / component.

[0152] The prediction unit, connected to the evaluation unit, is used to predict, based on the current lifespan stage and performance status, a function of the performance status of each device / component changing over time within a preset future time period starting from the current moment.

[0153] In an optional embodiment, the risk prediction parameter values ​​specifically include: the basic event probability of the fault tree model in the Living PSA model, the frequency of system failure-type initiating events and the frequency of equipment / component failure-type initiating events in the event tree model;

[0154] The first update module 2 specifically includes:

[0155] The first parameter update unit is used to obtain the basic event probability Q of the fault tree model at a preset future time based on the function of the performance state changing over time. (t) ;

[0156] The second parameter update unit, connected to the first parameter unit, is used to update the basic event probability Q. (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure initiation events of the event tree model at the preset future time.

[0157] The third parameter update unit is used to obtain the frequency of device / component failure initiation events at a preset future time based on the function of the performance state changing over time. IEi .

[0158] In an optional embodiment, the function of performance state over time is specifically a distribution function of the remaining useful life of the device / component.

[0159] In an optional embodiment, the first parameter update unit is specifically used for:

[0160] The probability Q of the basic event is obtained by calculating the following formula. (t) :

[0161]

[0162] Where: t is the preset future time; λ (s) The failure rate of the device / component corresponding to the basic event; T m The required time for the corresponding equipment / components to be put into operation to ensure safe system operation or mitigate accidents; θ (s) and θ (u) These are the expressions for the remaining useful life distribution functions of the corresponding devices / components, with s and u as time variables; s is the time variable from t to t+T. m The distribution function θ of the remaining useful life at any given time(s) The time variable used for integration; u is the distribution function θ of the remaining useful life from time 0 to time s. (u) The time variable used for integration.

[0163] In an optional embodiment, the third parameter update unit is specifically used for:

[0164] The frequency of the initiation event of the device / component failure type is obtained by calculating the following formula: IEi :

[0165]

[0166] Where: λ (t) The failure rate of the equipment / component corresponding to the initiation event of the equipment / component failure type; T is the average annual operating time of the corresponding equipment / component under a certain operating condition; θ (t) and θ (s) These are the expressions for the remaining useful life distribution function of the corresponding equipment / component, with t and s as time variables; t is the expression for the remaining useful life distribution function θ from time 0 to time T. (t) The time variable used for integration; s is the distribution function θ of the remaining useful life from time 0 to time t. (t) The time variable used for integration.

[0167] In an optional embodiment, the second update module specifically includes:

[0168] The receiving unit is used to receive the configuration of the nuclear power unit 10, which is automatically monitored by the DCS system or manually input through the human-machine interface. The configuration includes the state of the system / equipment of the nuclear power unit 10 at the preset future time, and to determine whether the state has changed.

[0169] An update unit, connected to the receiving unit, is used to update the event logic values ​​of the system / device in the Living PSA model if it is determined that the state has changed, so as to obtain the Living PSA model with the latest structure.

[0170] In an optional embodiment, the preset risk prediction value for future moments specifically includes at least one of the following:

[0171] The minimum cut set of the event tree / fault tree of the Living PSA model, the risk indicators of the nuclear power unit 10, the system failure probability, and the risk importance of the system / equipment.

[0172] In an optional embodiment, the system further includes:

[0173] The result combination module is connected to the risk prediction module 4 and is used to select multiple preset future times within a preset future time period, obtain the risk prediction values ​​of the multiple preset future times, and combine them to obtain the risk prediction result of the nuclear power unit 10 within the preset future time period.

[0174] In an optional embodiment, the result unification module specifically includes:

[0175] A risk prediction curve unit is used to obtain predicted risk indicators for the nuclear power unit 10 at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result of the nuclear power unit 10 within the preset future time period; and / or,

[0176] The predicted risk list unit is used to obtain the predicted risk importance of the key systems / equipment of the nuclear power unit 10 at multiple preset future times, establish a predicted risk list based on the relationship between the predicted risk importance and time, and visualize the predicted risk list as the risk prediction result of the nuclear power unit 10 in the preset future time period.

[0177] Example 3:

[0178] like Figure 10 As shown, Embodiment 3 of the present invention provides a nuclear power unit risk assessment system 20, which is set up in such a location as... Figure 2 The nuclear power plant 100 shown includes:

[0179] Risk monitoring module 21 is used to execute a method for risk monitoring based on the current performance status of each piece of equipment / component of the nuclear power unit 10; and,

[0180] Risk prediction function module 22 is used to execute the nuclear power unit risk prediction method as described in Example 1.

[0181] Specifically, in this embodiment, the nuclear power unit risk assessment system 20 can realize the risk assessment function of the nuclear power unit 10, including current risk monitoring and future risk prediction. The current risk monitoring function of the risk monitoring function module 21 can be implemented using the same method as the existing risk monitors in the nuclear power plant 100. The future risk prediction function of the risk prediction function module 22 is specifically implemented by setting sensors on relevant equipment / components of the nuclear power unit 10 to collect the operating parameters of the equipment / components. The nuclear power unit risk assessment system 20 receives the sensor data, obtains the function of the performance status of the equipment / components changing over time based on the received sensor data, and realizes the risk assessment of the nuclear power unit 10 based on the function of the performance status changing over time. Some functions of the risk monitoring function module 21 and the risk prediction function module 22 that have similarities can be implemented by the same program segments in the computer program. These program segments can be called to realize the functions of the risk monitoring function module 21 or the risk prediction function module 22, and there is no need to strictly distinguish between them.

[0182] The nuclear power unit risk prediction method, prediction system, and assessment system provided in Embodiments 1-3 of this invention predict the future progressive risks of nuclear power units based on the function of the performance status of nuclear power unit equipment / components changing over time. Building upon existing nuclear power unit risk monitoring technologies, this invention adds consideration to risk factors arising from changes in equipment performance status over time. It can accurately predict the future risk level of nuclear power units based on changes in equipment performance, improving the accuracy and predictability of nuclear power unit risk assessment. It can identify key weaknesses, guide nuclear power plant operation and maintenance personnel to conduct targeted configuration risk management, facilitate nuclear power unit risk prevention, enhance nuclear power unit operational safety, increase nuclear power unit availability, and ultimately improve its economic efficiency, thus improving both the safety and economics of nuclear power units.

[0183] Embodiments 1-3 of this invention propose real-time monitoring and prediction of nuclear power unit operational risks based on the actual performance level of equipment, and specifically provide the overall architecture and key functional module solutions for this system. Compared with the risk monitoring systems currently used in nuclear power plants, the system tools proposed in this invention can consider the risk influencing factor of equipment performance status changing over time, reflecting the differences in the lifespan stages of different units and equipment, and achieving more accurate monitoring and prediction of risks and their key contributors. The application of this system in nuclear power plants is expected to achieve the following effects:

[0184] Enhance operational safety: Provide more realistic risk indicators, predict and intuitively display changes in unit risk levels and key risk factors, help operation and maintenance personnel understand the unit risk level and development trend at any time, take targeted management measures, avoid entering a high-risk state, strengthen defense in depth, maintain sufficient safety margin, and improve operational safety;

[0185] Improve operational economy: Anticipate potential high risks to units in advance, guide maintenance personnel to develop response measures and implement preparations as early as possible, reduce unplanned downtime, shorten downtime for maintenance, and improve unit availability and economy.

[0186] 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 predicting the risks of nuclear power units, characterized in that, include: A function to obtain the performance status of each piece of equipment / component of a nuclear power unit as a function of time; The risk prediction parameters of the preset dynamic probabilistic security analysis (Living PSA) model at a preset future time are updated based on the function of the performance state changing over time, specifically including: Based on the function of the performance state changing over time, obtain the basic event probability Q of the fault tree model in the Living PSA model at the preset future time. (t) Specifically: The probability Q of the basic event is obtained by calculating the following formula. (t) : Where: t is the preset future time; λ (s) The failure rate of the device / component corresponding to the basic event; T m The required time for the corresponding equipment / components to be put into operation to ensure safe system operation or mitigate accidents; θ (s) and θ (u) These are the expressions for the remaining useful life distribution functions of the corresponding devices / components, with s and u as time variables; s is the time variable from t to t+T. m The distribution function θ of the remaining useful life at any given time (s) The time variable used for integration; u is the distribution function θ of the remaining useful life from time 0 to time s. (u) The time variable used for integration. Based on the function of performance state changing over time, obtain the frequency of device / component failure initiation events in the event tree model of the Living PSA model at the preset future time. IEi Specifically: The frequency of the initiation event of the device / component failure type is obtained by calculating the following formula: IEi : Where: λ (t) The failure rate of the equipment / component corresponding to the initiation event of the equipment / component failure type; T is the average annual operating time of the corresponding equipment / component under a certain operating condition; θ (t) and θ (s) These are the expressions for the remaining useful life distribution function of the corresponding equipment / component, with t and s as time variables; t is the expression for the remaining useful life distribution function θ from time 0 to time T. (t) The time variable used for integration; s is the distribution function θ of the remaining useful life from time 0 to time t. (t) The time variable used for integration; Determine whether the configuration of the nuclear power unit has changed at the preset future time. If so, update the structure of the Living PSA model according to the changed configuration. The risk prediction parameter values ​​are applied to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time.

2. The method according to claim 1, characterized in that, The function for obtaining the performance status of each piece of equipment / component of the nuclear power unit over time specifically includes: Obtain the operating parameters of each piece of equipment / component of the nuclear power unit at the current moment; The operating parameters are compared with the full lifecycle data of each device / component to obtain the current lifecycle stage and performance status of each device / component. Based on the current lifespan stage and performance status, a function is used to predict the change in performance status of each device / component over a preset future time period starting from the current moment.

3. The method according to claim 1, characterized in that... The step of updating the risk prediction parameter values ​​of the preset dynamic probabilistic security analysis (Living PSA) model at a preset future time based on the function of the performance state changing over time specifically includes: Using the basic event probability Q (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure class initiation events in the event tree model of the LivingPSA model at the preset future time.

4. The method according to claim 1, characterized in that, The function of performance status changing over time is specifically the distribution function of the remaining useful life of the device / component.

5. The method according to claim 1, characterized in that, The step of determining whether the configuration of the nuclear power unit has changed at the preset future time, and if so, updating the structure of the Living PSA model according to the changed configuration, specifically includes: Receive the configuration of the nuclear power unit automatically monitored by the DCS system or manually input through the human-machine interface. The configuration includes the state of the nuclear power unit's system / equipment at a preset future time, and determine whether the state has changed. If so, update the event logic values ​​of the system / device in the Living PSA model to obtain the Living PSA model with the latest structure.

6. The method according to claim 1, characterized in that, The predetermined risk prediction value for future moments specifically includes at least one of the following: The minimum cut set of the event tree / fault tree in the Living PSA model, the risk indicators of the nuclear power unit, the system failure probability, and the risk importance of the system / equipment.

7. The method according to any one of claims 1-6, characterized in that, After applying the risk prediction parameter values ​​to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time, the method further includes: Multiple preset future moments are selected within a preset future time period, and the risk prediction values ​​of the multiple preset future moments are obtained and combined to obtain the risk prediction result of the nuclear power unit within the preset future time period.

8. The method according to claim 7, characterized in that, The step of obtaining and combining multiple preset future time-time risk prediction values ​​to obtain the risk prediction result of the nuclear power unit within the preset future time period specifically includes: Obtain predicted risk indicators for the nuclear power unit at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result for the nuclear power unit within the preset future time period; and / or, The predicted risk importance of the key systems / equipment of the nuclear power unit at multiple preset future time points is obtained. A predicted risk list is established based on the relationship between the predicted risk importance and time, and the predicted risk list is visualized as the risk prediction result of the nuclear power unit within the preset future time period.

9. A nuclear power unit risk prediction system, characterized in that, include: The acquisition module is a function used to acquire the performance status of each device / component of a nuclear power unit over time. The first update module, connected to the acquisition module, is used to update the risk prediction parameter values ​​of the preset Living Probabilistic Security Analysis (LPSA) model at a preset future time based on the function of the performance state changing over time. Specifically, it includes: The first parameter update unit is used to obtain the basic event probability Q of the fault tree model in the Living PSA model at a preset future time, based on the function of the performance state of the nuclear power unit equipment / component changing over time. (t) Specifically: The probability Q of the basic event is obtained by calculating the following formula. (t) : Where: t is the preset future time; λ (s) The failure rate of the device / component corresponding to the basic event; T m The required time for the corresponding equipment / components to be put into operation to ensure safe system operation or mitigate accidents; θ (s) and θ (u) These are the expressions for the remaining useful life distribution functions of the corresponding devices / components, with s and u as time variables; s is the time variable from t to t+T. m The distribution function θ of the remaining useful life at any given time (s) The time variable used for integration; u is the distribution function θ of the remaining useful life from time 0 to time s. (u) The time variable used for integration. The third parameter update unit is used to obtain the frequency of equipment / component failure-type initiation events in the event tree model of the Living PSA model at a preset future time, based on the function of the performance state of the nuclear power unit equipment / component changing over time. IEi Specifically: The frequency of the initiation event of the device / component failure type is obtained by calculating the following formula: IEi : Where: λ (t) The failure rate of the equipment / component corresponding to the initiation event of the equipment / component failure type; T is the average annual operating time of the corresponding equipment / component under a certain operating condition; θ (t) and θ (s) These are the expressions for the remaining useful life distribution function of the corresponding equipment / component, with t and s as time variables; t is the expression for the remaining useful life distribution function θ from time 0 to time T. (t) The time variable used for integration; s is the distribution function θ of the remaining useful life from time 0 to time t. (t) The time variable used for integration; The second update module, connected to the first update module, is used to determine whether the configuration of the nuclear power unit has changed at the preset future time. If so, the structure of the Living PSA model is updated according to the changed configuration. A risk prediction module, connected to the second update module, is used to apply the risk prediction parameter values ​​to the Living PSA model with the latest structure to obtain the risk prediction value of the nuclear power unit at the preset future time.

10. The system according to claim 9, characterized in that, The first update module further includes: The second parameter update unit is used to utilize the basic event probability Q. (t) Analyze the fault tree model of the nuclear power unit system to obtain the frequency of system failure initiation events in the event tree model of the Living PSA model at the preset future time.

11. The system according to claim 10, characterized in that, The system also includes: The result combination module, connected to the risk prediction module, is used to select multiple preset future times within a preset future time period, obtain the risk prediction values ​​of the multiple preset future times, and combine them to obtain the risk prediction result of the nuclear power unit within the preset future time period.

12. The system according to claim 11, characterized in that, The result combination module specifically includes: A risk prediction curve unit is used to obtain predicted risk indicators for the nuclear power unit at multiple preset future times, plot the relationship between the predicted risk indicators and time on a coordinate graph to form a predicted risk curve, and visualize the predicted risk curve as the risk prediction result for the nuclear power unit within the preset future time period; and / or, The predicted risk list unit is used to obtain the predicted risk importance of the key systems / equipment of the nuclear power unit at multiple preset future times, establish a predicted risk list based on the relationship between the predicted risk importance and time, and visualize the predicted risk list as the risk prediction result of the nuclear power unit in the preset future time period.

13. A nuclear power unit risk assessment system, characterized in that, include: The risk monitoring function module is used to perform risk monitoring based on the performance status of each piece of equipment / component of the nuclear power unit at the current moment. as well as, The risk prediction function module is used to execute the nuclear power unit risk prediction method as described in any one of claims 1-8.

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