Nuclear Reactor Risk-Guided Multi-Attribute Autonomous Operation Decision-Making Method and System

Abstract

The present disclosure provides a risk-guided multi-attribute autonomous operation decision-making method and system for a nuclear reactor. The method includes: establishing multiple alternative operation decisions for the nuclear reactor, where the alternative operation decisions include the expected losses of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected losses satisfy preset loss conditions; simulating the nuclear reactor based on each alternative operation decision to obtain the expected parameter values of the operation state parameters of the nuclear reactor and the expected utility values of the corresponding effect variables during the simulation process, substituting the corresponding expected utility values into the utility function, and calculating the actual utility values corresponding to each utility variable; and determining the target operation decision of the nuclear reactor from multiple alternative operation decisions based on the actual utility values. The present disclosure uses the multi-attribute utility theory algorithm to quantify the impacts of different alternative operation decisions on the nuclear reactor, make quick and accurate decisions in a complex environment, and reduce the labor cost.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear reactor operation decision-making, and in particular to a risk-guided multi-attribute autonomous operation decision-making method and system for nuclear reactors. Background Art

[0002] Nuclear reactor control systems are typically used to maintain the state parameters of a nuclear reactor within a specified operating range. If these state parameters deviate from the specified range, it can lead to transient conditions in the nuclear power plant and pose a severe challenge to the accident mitigation systems of the nuclear power plant. Therefore, when a transient condition occurs in a nuclear reactor, how the reactor control system makes correct operation decisions to ensure the stable operation of the nuclear reactor has become an important concern. However, there are a relatively large number of alternative options, and in order to accurately determine the target decision, it takes a lot of effort to determine the operation decision manually, the decision-making efficiency is low, and the labor cost is high.

[0003] Currently, in the field of nuclear reactors, static decision-making is usually carried out through deterministic decision analysis. During the deterministic decision analysis process, the influence of random or uncertain factors is not considered, and the results of the decision are predicted based on determined parameters, and the risks and uncertainties caused by parameter uncertainty or random fluctuations cannot be captured. Static decision-making does not consider the dynamic changes of time, but analyzes and selects for a specific moment or state, resulting in the target operation decision obtained by static decision-making being unable to adapt to the complex situations that change over time and unable to consider possible future changes. Therefore, it is not flexible enough and unable to cope with emergencies. Therefore, the accuracy and effectiveness of static decision-making through deterministic decision analysis are poor. Summary of the Invention

[0004] The technical problem to be solved by the present disclosure is to overcome the deficiencies of the prior art, that is, in the deterministic nuclear reactor operation decision-making method for a single target, uncertainties are not considered, the operation scenarios cannot be dynamically matched, and it is not applicable to complex decision-making scenarios with multiple attributes, and to provide a risk-guided multi-attribute autonomous operation decision-making method and system for nuclear reactors.

[0005] The present disclosure solves the above technical problems through the following technical solutions:

[0006] In a first aspect, a risk-guided multi-attribute autonomous operation decision-making method for a nuclear reactor is provided. The risk-guided multi-attribute autonomous operation decision-making method for a nuclear reactor includes:

[0007] Establish a plurality of alternative operation decisions for the nuclear reactor, where the alternative operation decisions include the expected losses of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected losses meet the preset loss conditions;

[0008] The nuclear reactor is simulated based on each alternative operation decision to obtain expected parameter values ​​of the operation state parameters of the nuclear reactor and expected utility values ​​of the corresponding effect variables during the simulation process, and the corresponding expected utility values ​​are substituted into the utility function to calculate the actual utility values ​​of the utility variables; wherein the utility function represents the corresponding relationship between the operation state parameters and the utility variables, and the actual utility value of the utility variable corresponding to the state parameter whose parameter value does not exceed the safety range is greater than the actual utility value of the utility variable corresponding to the state parameter whose parameter value exceeds the safety range;

[0009] Based on the actual utility value, a target operating decision for the nuclear reactor is determined from a plurality of alternative operating decisions.

[0010] Optionally, the utility function is obtained by the following steps:

[0011] For each of the operating status parameters, a utility function is obtained by performing an affine transformation on the probability density distribution function of the operating status parameter.

[0012] Optionally, the probability density distribution function is represented by Gaussian distribution.

[0013] Optionally, the step of establishing a plurality of alternative operating decisions for the nuclear reactor comprises:

[0014] According to the success probability of each sequence of the decision tree, the alternative operation decisions are screened out; each sequence represents an operation decision; the success probability is related to the expected loss;

[0015] and / or, updating the success probability of the alternative operation decision in the decision tree according to the system equipment status of the nuclear reactor monitored in real time;

[0016] Arrange the sequences in reverse order according to the success probabilities, and determine the operation decisions corresponding to the sequences whose success probabilities are greater than a probability threshold as the alternative operation decisions; or determine the operation decisions corresponding to a preset number of sequences that are ranked first as the alternative operation decisions.

[0017] Optionally, the step of determining the target operating decision of the nuclear reactor from a plurality of alternative operating decisions based on the actual utility value comprises:

[0018] determining a weight corresponding to the utility variable;

[0019] For each of the alternative operation decisions, weighted summing the actual utility values ​​of the utility variables according to the weights, and calculating the composite utility of the alternative operation decisions based on the weighted results;

[0020] Determine the alternative operation decision with the maximum composite utility as the target operation decision.

[0021] Optionally, the step of calculating the composite utility of the alternative operation decision based on the weighted result includes:

[0022] Multiply the probability of success by the weighted result to obtain the composite utility of the alternative operation decision.

[0023] Optionally, the effect variable is obtained by the following formula:

[0024]

[0025] where x i is the i-th utility variable, p i is the i-th operation state parameter; (p i ) max is the maximum value of the i-th operation state parameter, (p i ) min is the minimum value of the i-th operation state parameter;

[0026] And / or, the weight is obtained by the following formula:

[0027]

[0028] where N represents the total number of utility variables, ω j represents the weight corresponding to the j-th utility variable;

[0029] And / or, the expression of the probability density distribution function is as follows:

[0030]

[0031] where p(x|μ,σ) represents the probability density distribution function, x represents the utility variable, μ represents the mean of the probability density distribution, σ represents the standard deviation of the probability density distribution, and e represents the base of the natural logarithm;

[0032] And / or, the expression of the utility function is as follows:

[0033]

[0034] where u(x|μ,σ) represents the utility function, a and b are transformation coefficients of the affine transformation, x represents the utility variable, μ represents the mean of the probability density distribution, σ represents the standard deviation of the probability density distribution, and e represents the base of the natural logarithm; the transformation coefficients a and b of the affine transformation are calculated by the following formula:

[0035] a + b = 1

[0036]

[0037] and / or, the composite utility is obtained by the following formula:

[0038]

[0039] where U represents the composite utility, N represents the total number of the utility variables, ω i represents the weight corresponding to each utility variable, x i represents the i-th utility variable, and u(x i ) represents the value of the dependent variable of the utility function, that is, the actual utility value of the utility variable.

[0040] In a second aspect, a risk-guidance multi-attribute autonomous operation decision-making system for a nuclear reactor is provided. The risk-guidance multi-attribute autonomous operation decision-making system for a nuclear reactor includes:

[0041] A probabilistic decision analysis module, configured to establish multiple alternative operation decisions for the nuclear reactor, where the alternative operation decisions include the expected loss of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected loss meets a preset loss condition;

[0042] A deterministic decision analysis module, configured to simulate the nuclear reactor based on each alternative operation decision to obtain the expected parameter values of the operation state parameters and the expected utility values of the corresponding effect variables during the simulation process, and substitute the corresponding expected utility values into the utility function to calculate and obtain the actual utility values corresponding to the utility variables; where the utility function characterizes the corresponding relationship between the operation state parameters and the utility variables, and the actual utility value of the utility variable corresponding to the state parameter whose parameter value does not exceed the safety range is greater than the actual utility value of the utility variable corresponding to the state parameter whose parameter value exceeds the safety range;

[0043] An optimal decision generation module, configured to determine the target operation decision of the nuclear reactor from multiple alternative operation decisions based on the actual utility values.

[0044] Optionally, the utility function is obtained through the following steps: for each of the operation state parameters, an affine transformation is performed on the probability density distribution function of the operation state parameter to obtain the utility function.

[0045] Optionally, the probability density distribution function is represented by a Gaussian distribution.

[0046] Optionally, the probabilistic decision analysis module includes:

[0047] A decision screening unit, configured to screen out the alternative operation decisions according to the success probability of each sequence of the decision tree; each sequence represents an operation decision; the success probability is related to the expected loss;

[0048] And / or, a success probability updating unit, configured to update the success probability of the alternative operation decisions in the decision tree according to the system device status of the nuclear reactor monitored in real time;

[0049] And / or, a sorting unit, configured to reverse-sort the sequences according to the success probability, and determine the operation decisions corresponding to the sequences with the success probability greater than the probability threshold as the alternative operation decisions; or, the sorting unit is configured to determine the operation decisions corresponding to a preset number of sequences ranked at the front as the alternative operation decisions.

[0050] Optionally, the optimal decision generation module includes:

[0051] A weight determination unit, configured to determine the weights corresponding to the utility variables;

[0052] A weighted summation unit, configured to, for each of the alternative operation decisions, perform weighted summation on the actual utility values of the utility variables according to the weights, and calculate the composite utility of the alternative operation decisions based on the weighted results;

[0053] A target operation decision determination unit, configured to determine the alternative operation decision with the maximum composite utility as the target operation decision.

[0054] Optionally, the weighted summation unit is specifically configured to: multiply the success probability by the weighted result to obtain the composite utility of the alternative operation decision.

[0055] In a third aspect, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the nuclear reactor risk-guided multi-attribute autonomous operation decision method described in any one of the above is implemented.

[0056] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the nuclear reactor risk-guided multi-attribute autonomous operation decision method described in any one of the above is implemented.

[0057] On the basis of conforming to the common knowledge in the art, the above preferred conditions can be combined arbitrarily to obtain various preferred examples of the present disclosure.

[0058] The positive and progressive effects of the present disclosure are as follows: The present disclosure uses the multi-attribute utility theory algorithm as a mathematical analysis tool, which can comprehensively consider multiple operating state parameters, establish a unified scale to measure the value of multiple alternative operating decisions, or measure the satisfaction of decision-makers with multiple alternative operating decisions, realize cross-criteria comparison and decision-making, calculate the actual utility value of the utility variable, quantify the impact of different alternative operating decisions on the nuclear reactor through the actual utility value, obtain accurate target operating decisions, and can make quick and accurate decisions in a complex and changeable environment to ensure the safe and stable operation of the nuclear reactor, improve the safety and safety management level of the nuclear reactor, greatly reduce the labor cost, improve the scientificity and accuracy of decision-making, reduce decision-making risks, and provide strong support for engineering practice.

[0059] Furthermore, the present disclosure can update the success probability of alternative operating decisions in the decision tree according to the system equipment status of the nuclear reactor monitored in real time, perform dynamic decision-making, and can consider possible future changes, so that the determined target operating decision can adapt to complex situations that change over time.

[0060] Furthermore, based on the multi-attribute utility theory, the present disclosure realizes autonomous operating decision-making for multiple objectives of the nuclear reactor. By integrating multiple objectives, a more comprehensive and balanced target operating decision can be found, improving the quality and effectiveness of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flowchart of a risk-guidance multi-attribute autonomous operating decision-making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure;

[0062] Figure 2 is a success probability diagram of multiple sequences of a decision tree in a risk-guidance multi-attribute autonomous operating decision-making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure;

[0063] Figure 3 is a relationship diagram between utility variables and operating state parameters in a risk-guidance multi-attribute autonomous operating decision-making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure;

[0064] Figure 4 is another flowchart of a risk-guidance multi-attribute autonomous operating decision-making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure;

[0065] Figure 5 is a flowchart of weight optimization in a risk-guidance multi-attribute autonomous operating decision-making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure;

[0066] Figure 6The distribution curve graph of the probability density distribution function in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0067] Figure 7 The schematic diagram of the utility function after affine transformation in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0068] Figure 8 The schematic diagram of the calculation process in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0069] Figure 9 The framework diagram of a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0070] Figure 10 The schematic diagram of the relationship between the core outlet temperature and time in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0071] Figure 11 The schematic diagram of the relationship between the utility variable corresponding to the core outlet temperature and time in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0072] Figure 12 The schematic diagram of the utility functions corresponding to each utility variable in a nuclear reactor risk - guided multi - attribute autonomous operation decision - making method provided by an exemplary embodiment of the present disclosure;

[0073] Figure 13 The module schematic diagram of a nuclear reactor risk - guided multi - attribute autonomous operation decision - making system provided by an exemplary embodiment of the present disclosure;

[0074] Figure 14 The structural schematic diagram of an electronic device provided by an exemplary embodiment of the present disclosure. Detailed implementation manners

[0075] The present disclosure will be further described below by way of embodiments, but the present disclosure is not limited to the scope of the described embodiments.

[0076] In the embodiments of the present disclosure, prefix words such as "first" and "second" are only used to distinguish different described objects, and have no restrictive effect on the position, order, priority, quantity, content, etc. of the described objects. The use of prefix words such as ordinal numbers for distinguishing described objects in the embodiments of the present disclosure does not constitute a limitation on the described objects. For the statement of the described objects, refer to the description in the claims or the context of the embodiments, and no redundant limitation should be formed due to the use of such prefix words. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0077] Embodiment 1

[0078] Decision-making is one of the basic cognitive processes of human behavior and is a process of determining and selecting alternative solutions. In this process, people can select a target decision from multiple alternative solutions according to specific criteria for controlling a target object. Each alternative solution provides a different method or path, and by executing the alternative solution, the target object can be transformed from a preset state or condition to an ideal state or condition.

[0079] Among them, the target object can be selected according to the actual situation. The alternative solutions in this embodiment are specifically alternative operation decisions, which can be selected according to the actual situation and are not particularly limited herein. The target object in this embodiment is a nuclear reactor. It should be noted that the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method in this embodiment can not only be used to control the nuclear reactor, but also be used to control other target objects, which can be selected according to the actual situation and are not particularly limited herein.

[0080] In engineering practice, the determination of the target decision is an important link in solving decision-making problems. Due to the nature of some decision-making problems, the alternative solutions are relatively diverse and numerous, and a large number of alternative solutions need to be comprehensively considered. In order to accurately determine the target decision, a large amount of human cost is required, and manual judgment is subjective and cannot ensure the accuracy of the determination of the target decision. Especially in the face of complex decision-making scenarios with multiple objectives and multiple alternative solutions, how to comprehensively consider multiple operating state parameters and make a comprehensive and scientific decision scientifically and effectively to determine an accurate target decision has become an important issue.

[0081] The nuclear reactor control system is generally designed to maintain the operating state parameters of the nuclear reactor within a specified operating range. The operating state parameters include at least one of the following: reactor power, coolant flow rate, power-to-flow ratio, reactor outlet temperature, coolant level, and turbine state. The above-listed operating state parameters are only examples and can be selected according to the actual situation and are not particularly limited herein.

[0082] If the parameter value of the operating status parameter exceeds the specified safety range, for example, the parameter value is greater than the upper limit value of the safety range or the parameter value is less than the lower limit value of the safety range, it will lead to a transient situation in the nuclear power plant and pose a severe challenge to the accident mitigation system of the nuclear power plant, including a challenge to the safety system of the nuclear power plant. Therefore, when a transient situation occurs in the nuclear reactor, how the nuclear reactor control system makes correct decisions and executes the decision operation plan to ensure the stable operation of the reactor has become an important concern.

[0083] Utility theory is mainly applied in economics to describe an individual's preference or satisfaction degree for a certain choice or result. The multi-attribute utility theory is an efficient quantitative comparison method and an indispensable important part of modern decision science. With attributes as evaluation criteria, the multi-attribute utility theory is applicable to evaluating complex decision-making scenarios with multiple attributes and a large number of alternative operation decisions. The multi-attribute utility theory quantifies each evaluation index into a utility value, multiplies the utility value of each alternative operation decision by the corresponding weight, and then performs weighted synthesis according to the synthesis model to calculate the composite utility. Following the principle of maximizing utility, the alternative operation decision with the maximum composite utility can be selected as the target operation decision.

[0084] The multi-attribute utility theory is usually applicable to decision-making problems where alternative solutions have multiple attributes or multiple criteria and is an important tool for decision analysis in the engineering field. It realizes cross-criteria comparison and decision-making by converting quantitative data under different criteria into a common utility value. When dealing with complex decision-making problems, the multi-attribute utility theory shows its unique advantages in many aspects. It realizes cross-criteria comparison through dimensionless processing in the decision-making process, constructs a utility function to independently calculate the utility values of each attribute, and considers the decision-maker's risk attitude and preference through weighting.

[0085] When a transient situation occurs in the nuclear reactor, through the multi-attribute utility theory, only a minimal amount of effort is required and there is no need to increase the workload too much. Multiple alternative operation decisions can be introduced for evaluation in decision analysis. Once the utility function is constructed, the actual utility value can be obtained according to the utility function, and the alternative operation decisions can be evaluated based on the actual utility value. When the number of alternative operation decisions is large, any number of alternative operation decisions can be evaluated without being limited by the quantity, and then the target operation decision can be determined, greatly improving the decision-making efficiency.

[0086] The present disclosure proposes a risk-guided multi-attribute autonomous operation decision-making method for a nuclear reactor, which uses the multi-attribute utility theory as a mathematical analysis tool to ensure that various attributes and factors can be comprehensively considered, and then constructs a structured comprehensive decision analysis framework.

[0087] Figure 1The flowchart of a risk - guided multi - attribute autonomous operation decision - making method for a nuclear reactor provided by an exemplary embodiment of the present disclosure. The risk - guided multi - attribute autonomous operation decision - making method for a nuclear reactor includes:

[0088] Step S101: Establish multiple alternative operation decisions for the nuclear reactor. The alternative operation decisions include the expected loss of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected loss satisfies a preset loss condition;

[0089] Among them, the operation state parameters are used to detect whether the nuclear reactor fails, and the expected utility value is within a preset range. Each alternative operation decision is mainly composed of an unequal number of control actions. The component information of the nuclear reactor failure or performance degradation before executing the alternative operation decision, as well as the sensor and reactor state information, are used. After execution, the nuclear reactor can resume stable operation. For example, assuming that the control actions include reducing the power of the nuclear reactor, etc. Before executing the alternative operation decision, the sensor detects that the nuclear reactor has a failure or performance degradation, etc. After executing the alternative operation decision, the nuclear reactor can resume stable operation.

[0090] The probabilistic decision - analysis module is used to execute step 101, obtain the alternative operation decisions, and send them to the deterministic decision - analysis module for executing step 102. Although the probabilistic decision - analysis module automatically generates a corresponding set of control actions for each alternative operation decision, it cannot determine the performance parameters of the components associated with the control actions. The control actions generated by the probabilistic decision - analysis module only contain abstract action concepts without specific descriptions. For example, the control action may be to reduce the power, but it does not specify how much power needs to be reduced. Therefore, it is necessary to input the alternative operation decisions determined by the probabilistic decision - analysis module into the deterministic decision - analysis module for further evaluation and analysis.

[0091] The expected loss satisfying the preset loss condition can be the minimization of the expected loss, or the expected loss is less than a loss threshold. The loss threshold can be selected according to the actual situation and is not specifically limited here.

[0092] See Figure 2 , the decision tree in the present invention is in the form of an event tree in probabilistic safety assessment. Different from the event tree in probabilistic safety assessment, the expected consequence of the decision tree in the present invention is to avoid triggering a reactor trip, calculate the success probability of each sequence, rather than the core damage frequency.

[0093] Figure 2The steam turbine control valve in it moves in the closing direction, the steam turbine control valve resets, SGBV1 opens, SGBV2 opens, the feed water flow rate of the steam turbine bypass valve, the feed water flow rate to steam generator 1 is reduced, the feed water flow rate to steam generator 2 is reduced, steam turbine bypass valve 1 is opened, steam turbine bypass valve 2 is opened, the power of reactor module 1 is reduced, the power of reactor module 2 is reduced, steam turbine bypass valve 1 is closed, steam turbine bypass valve 2 is closed, manually shutting down reactor module 1 and manually shutting down reactor module 2 constitute alternative operating decisions, that is, each sequence of the decision tree.

[0094] It should be noted that the above sequences are only for examples and can be selected according to the actual situation, and no special limitations are made here.

[0095] The state evolution of the nuclear reactor can be monitored through operating state parameters, and the quantity and type of operating state parameters can be selected according to the actual situation, and no special limitations are made here.

[0096] Table 1 lists some operating state parameters of a liquid metal reactor and their rated steady-state values.

[0097] Table 1 Some system design variables of an advanced liquid metal reactor and the corresponding rated steady-state values

[0098]

[0099] Considering the complexity of the nuclear reactor system and potential safety risks, an effective decision-making method must be able to comprehensively consider operating state parameters in multiple different dimensions. The selection of operating state parameters needs to consider multiple factors, such as the operating state of the nuclear reactor, the performance of the cooling system, the temperature distribution of the fuel rods, etc., and can be selected according to the actual situation, and no special limitations are made here.

[0100] Since the dimensions of multiple operating state parameters are different, it is difficult to measure them with a single index. Therefore, analyzing the operating decisions of nuclear reactors based on multi-attribute utility theory has certain potential practical value.

[0101] The selection of operating state parameters generally chooses shutdown variables within the safe range. A shutdown variable is a variable that can be used to judge the shutdown of a nuclear reactor. The basic goal of the nuclear reactor control system to select the target operating decision is to keep the state of the nuclear power plant within the controllable state. If it exceeds the challenge surface surrounded by the shutdown variables, the nuclear reactor protection system and / or the dedicated safety facility drive system will be activated. Table 2 lists the shutdown variables of a liquid metal reactor and their functions. Failures include but are not limited to shutdowns, and can be selected according to the actual situation, and no special limitations are made here. The controllable state means that no failure has occurred in the nuclear power plant. The monitoring variables in Table 2 are the shutdown variables, and the safety function is which type of monitoring object the monitoring variable specifically monitors.

[0102] Table 2 Shutdown Variables of a Certain Advanced Liquid Metal Reactor and Their Related Safety Functions

[0103]

[0104] The basic objective of the reactor control system is to keep the state of the nuclear power plant within a controllable range. If it exceeds the challenge surface enclosed by the shutdown variables, the reactor protection system and / or the dedicated safety facility drive system will be activated.

[0105] Due to the large variety of operating state parameters and their different measurement units (dimensions), it is difficult to directly judge the advantages and disadvantages of each alternative operating decision based on the numerical magnitude, which affects the reliability of the obtained target operating decision. When conducting decision analysis, it is necessary to eliminate the non-additivity caused by the different measurement units of each operating state parameter, so that the statistical indicators are converted into additive values, and the corresponding relationship between the operating state parameters and the utility variables in the alternative operating decisions is established.

[0106] Figure 3 It is the relationship between a certain operating state parameter and the utility variable, where the horizontal axis is the parameter value of the operating state parameter and the vertical axis is the expected utility value of the utility variable.

[0107] In one embodiment, in order to more intuitively compare the effects of different alternative operating decisions on the operating state parameters, it is necessary to normalize the operating state parameters, that is, transform the operating state parameters to the interval [0,1].

[0108] Using the standard 0-1 transformation, the operating state parameters are linearly transformed through the following formula to obtain the utility variable:

[0109]

[0110] where, x i is the i-th utility variable, p i is the i-th operating state parameter; (p i ) max is the maximum value of the i-th operating state parameter, (p i ) min is the minimum value of the i-th operating state parameter. The safety range can be determined according to the technical specifications of the nuclear power plant and can be set according to the actual situation, and will not be specifically limited here. Table 3 lists the utility variables of a certain liquid metal reactor.

[0111] Table 3 Utility Variables of a Certain Advanced Liquid Metal Reactor

[0112]

[0113] The safety variables in Table 3 are the operating state parameters. Among them, the maximum value refers to the maximum value of the change range of the operating state parameters, the minimum value refers to the minimum value of the change range of the operating state parameters, and the upper and lower boundaries are the abscissa values when the ordinate of the probability density distribution function of the Gaussian distribution is 0, respectively.

[0114] Step S102: Simulate the nuclear reactor based on each alternative operating decision to obtain the expected parameter values of the operating state parameters of the nuclear reactor during the simulation process and the expected utility values of the corresponding effect variables, and substitute the corresponding expected utility values into the utility function to calculate the actual utility values corresponding to each utility variable.

[0115] Among them, the utility function represents the corresponding relationship between the operating state parameters and the utility variables. The actual utility values of the utility variables corresponding to the operating state parameters whose parameter values do not exceed the safety range are greater than the actual utility values of the utility variables corresponding to the operating state parameters whose parameter values exceed the safety range.

[0116] The utility function needs to be able to reflect the influence of different values of the operating state parameters on the pros and cons of the alternative operating decisions. When applying the utility theory in the economic field, the value range of the expected utility value is [0, 1]. However, when applying the utility theory in the field of nuclear reactors, one of the decision-making goals is to ensure the safety and stability of the nuclear reactor, and constraint conditions need to be met during the decision-making process. If the operating state parameters exceed the preset safety range, negative actual utility values are required to give risk warnings to the alternative operating decisions. Therefore, when applying the utility theory in the field of nuclear reactors, the multi-attribute autonomous operating decision method guided by nuclear reactor risk in this embodiment expands the value range of the actual utility value to [-∞, 1].

[0117] The following further explains the constraint conditions and the value range of the actual utility value.

[0118] The constraint condition is that the operating state parameters cannot exceed the preset safety range. The safety range, the lower limit value and the upper limit value of the safety range can be selected according to the actual situation and are not specifically limited here. It can be understood that if the operating state parameters exceed the safety range, it means that the stable operation of the nuclear reactor cannot be guaranteed, and the nuclear reactor may malfunction. Therefore, it is necessary to ensure that after controlling the nuclear reactor according to each alternative operating decision, the operating state parameters can all be within the safety range.

[0119] Therefore, when constructing the utility function, for the operating state parameters whose parameter values are at the boundaries of the safe range, that is, the parameter values of the operating state parameters are at the lower limit value and the upper limit value of the safe range, the actual utility value is 0; for the operating state parameters whose parameter values are within the safe range, relatively high actual utility values should be given, while for the operating state parameters whose parameter values exceed the safe range, relatively low actual utility values should be given. Once the parameter value of the operating state parameter exceeds the safe range, the actual utility value will be reduced to a negative value, even negative infinity, to avoid the decision-making process selecting alternative operating decisions that may lead to safety risks. When applying the utility theory in the field of nuclear reactors, the value range of the actual utility value is extended to [-∞, 1].

[0120] When constructing the utility function, within the safe range delimited by the lower limit value and the upper limit value, it is necessary to make the value of the dependent variable of the utility function positive, and in the area beyond the safe range, the value of the dependent variable of the utility function is negative. It can be understood that if the operating state parameter exceeds the safe range, the actual utility value of the utility variable corresponding to the operating state parameter is relatively small.

[0121] In the multi-attribute utility theory, a utility function is constructed for each operating state parameter. Through the utility function, the parameter value of the operating state parameter is converted into the actual utility value of the utility variable, that is, the quantitative data under different criteria are uniformly converted into a dimensionless value between 0 and 1, which plays a role of normalization, reflecting the decision-maker's preferences or satisfactions for different operating state parameters of different alternative operating decisions, so as to realize cross-criteria comparison and decision-making.

[0122] Step S103: Based on the actual utility value, determine the target operating decision of the nuclear reactor from multiple alternative operating decisions.

[0123] See Figure 4 , the above nuclear reactor risk-guided multi-attribute autonomous operation decision-making method can be implemented through a decision-making system. The decision-making system is integrated into the nuclear reactor control system. The decision-making system is specifically used for the two functions of real-time monitoring and automatic start of the decision-making process. The decision-making system real-time monitors the operating state of the nuclear reactor, that is, monitors each operating state parameter of the nuclear reactor, judges whether the nuclear reactor fails according to the operating state parameter. When the nuclear reactor fails, it automatically starts the decision-making process, that is, executes the above nuclear reactor risk-guided multi-attribute autonomous operation decision-making method, determines the target operating decision of the nuclear reactor from multiple alternative operating decisions, can quickly identify problems and take corresponding control measures, effectively prevent accidents from occurring, ensure the safe operation of the nuclear reactor, not only can improve the accuracy and efficiency of reactor fault control, but also can reduce decision-making errors caused by human factors, and better cope with various uncertainties and emergencies.

[0124] Among them, the definition of the fault state can be set according to the actual situation. For example, the parameter values of a preset number of operating state parameters exceed the state threshold. The preset number and the state threshold can be specifically set according to the actual situation and are not particularly limited herein.

[0125] In this embodiment, the multi-attribute utility theory algorithm is used as a mathematical analysis tool, which can comprehensively consider multiple operating state parameters, establish a unified scale to measure the value of multiple alternative operating decisions, or measure the satisfaction of the decision maker with multiple alternative operating decisions, realize cross-standard comparison and decision-making, quantify the impact of different alternative operating decisions on the nuclear reactor through the actual utility value, determine the target operating decision based on the actual utility value, so as to obtain an accurate target operating decision, be able to make quick and accurate decisions in a complex and changeable environment, ensure the safe and stable operation of the nuclear reactor, improve the safety and safety management level of the nuclear reactor, greatly reduce the labor cost, improve the scientificity and accuracy of decision-making, reduce the decision-making risk, provide strong support for engineering practice, integrate the current operating state parameters and the expected operating state parameters of the nuclear reactor into the process of formulating the target operating decision, so as to generate a unique target operating decision.

[0126] As a decision analysis framework, the multi-attribute utility theory allows decision makers to evaluate the utility of different options in complex decision-making problems with multiple attributes or characteristics, comprehensively consider the impacts of multiple dimensions, so as to make the best choice.

[0127] During the operation of the nuclear reactor, it will face various potential risk challenges, such as multiple risk challenges like equipment aging, operation errors, external environment changes, etc. The multi-attribute utility theory can help managers quantify risks and assign weights, so as to accurately determine the target operating decision. Through the multi-attribute utility theory, multiple aspects such as the safety, economy, and environmental impact of the nuclear reactor can be taken into consideration to achieve a comprehensive evaluation.

[0128] In today's complex engineering systems, the decision-making process often involves multiple interrelated attributes, that is, operating state parameters, and multiple aspects such as risks, performance, and costs need to be comprehensively considered. In the nuclear power field, especially when transient situations occur in the nuclear reactor, the success or failure of the target operating decision not only concerns the stability and safety of the nuclear reactor, but also directly relates to the safety of the surrounding personnel and environmental stability. Using the risk-guided multi-attribute autonomous operating decision-making method for nuclear reactors in this embodiment, scientific and effective decision-making can be achieved in a complex nuclear reactor environment.

[0129] In one embodiment, step S103 includes: determining the weights corresponding to the utility variables.

[0130] Among them, the weights corresponding to each utility variable are equal.

[0131] Using the uniform weight allocation method, equal weights are assigned to each utility variable, that is, equal importance is attached to each utility variable. The weights can be calculated by the following formula:

[0132]

[0133] where N represents the total number of utility variables, and ω j represents the weight corresponding to the j-th utility variable.

[0134] In this embodiment, the uniform weight allocation method is adopted to assign equal weights to each utility variable and attach equal importance to each utility variable, ensuring that each utility variable is treated equally in the decision-making process, and to a certain extent, avoiding decision-making biases caused by subjective biases or improper weight allocation, so as to ensure the safe and stable operation of the nuclear reactor and improve the safety and safety management level of the nuclear reactor.

[0135] In order to more comprehensively reflect the actual contributions of various utility variables in the decision-making process and improve the scientificity and accuracy of decision-making, other more refined and effective methods can be introduced to optimize the assignment of weights, ensuring that each utility variable is assigned appropriate weights, so that the determined target operation decision is more in line with the actual situation and meets the engineering requirements.

[0136] As Figure 5 shown, Figure 5 the attribute information in is the operating state parameter, the fault scenario represents the scenario where the nuclear reactor fails, and both the optimal decision result and the historical optimal decision represent the target operation decisions obtained historically, which can be used as a reference for determining the current target operation decision. On the premise of the existence of a large amount of applicable historical data (such as the optimal decision results and attribute information under fault scenarios) and expert knowledge, the following weight assignment optimization ideas can be proposed: Define a utility function and obtain the composite utility, which is used to reflect the performance of the decision-making under different weight combinations, that is, to reflect the composite utility of the alternative operation decisions under different weight combinations. Based on historical data and expert knowledge, use the target optimization algorithm to iteratively find the optimal weight combination. During the verification process, retain the weight combination that is consistent with the historical optimal decision, and output the fault scenario and the corresponding weight combination.

[0137] It can be understood that when determining the historical optimal decision, a process of determining weights will be experienced. The historical optimal decision is calculated based on the actual utility values and weights. Retaining the weight combination that is consistent with the historical optimal decision means retaining the same weight combination as the one used when determining the historical optimal decision.

[0138] If the same fault occurs in the nuclear reactor later, the weight combination can be applied to the same fault scenario to achieve precise decision-making. The whole process combines data-driven and expert knowledge, aiming to improve the scientificity and accuracy of decision-making by optimizing weight allocation.

[0139] The target optimization algorithm can be selected according to the actual situation and is not particularly limited here.

[0140] In one embodiment, the utility function is obtained through the following steps: for each operating state parameter, the utility function is obtained by performing an affine transformation on the probability density distribution function of the operating state parameter.

[0141] In this embodiment, according to the proprietary characteristics of the nuclear reactor field, the utility function is constructed through affine transformation, realizing the quantification of the utility value of the utility variable. Furthermore, comprehensive decision-making analysis can be carried out based on the actual utility value, improving the scientificity and accuracy of decision-making, reducing decision-making risks, and providing strong support for engineering practice.

[0142] In one embodiment, the probability density distribution function is represented by a Gaussian distribution.

[0143] In order to enable the effective application of utility theory in the engineering field, the definition of the utility function in utility theory is extended to meet specific needs and requirements.

[0144] In the practical application in the engineering field, since the parameter values of the operating state parameters of the nuclear reactor show a random distribution characteristic, and the Gaussian distribution, as a continuous probability distribution, can better fit this randomness. Therefore, the Gaussian distribution is used to represent the probability density distribution function of the operating state parameters of the nuclear reactor.

[0145] The representation of the probability density distribution function is as follows:

[0146]

[0147] Among them, p(x|μ,σ) represents the probability density distribution function, x represents the utility variable, μ represents the mean of the probability density distribution, σ represents the standard deviation of the probability density distribution, and e represents the base of the natural logarithm.

[0148] Figure 6 The normal distribution diagrams with different standard deviations are shown, where the horizontal axis is the utility variable x, the vertical axis is the probability density distribution function p(x|μ,σ), and μ = 0.5.

[0149] In this embodiment, μ = 0.5, which can be specifically selected according to the actual situation and is not particularly limited here.

[0150] The affine transformation of the probability density distribution function of the Gaussian distribution is performed through the following formula:

[0151]

[0152] Among them, u(x|μ,σ) represents the utility function, a and b are the transformation coefficients of the affine transformation, x represents the utility variable, μ represents the mean of the probability density distribution, σ is the standard deviation representing the probability density distribution, and e represents the base of the natural logarithm.

[0153] Figure 7 The utility function obtained after the affine transformation is shown. The vertical axis represents the utility function, and the horizontal axis represents the utility variable. The curve of the utility function intersects the horizontal axis at two points, which are the lower limit value and the upper limit value of the safe state respectively.

[0154] The transformation coefficients a and b of the affine transformation can be obtained by solving the following system of equations:

[0155] a + b = 1

[0156]

[0157] In this embodiment, as a continuous probability distribution, the Gaussian distribution can better fit this randomness. Therefore, using the Gaussian distribution to represent the probability density distribution function of the operating state parameters of the nuclear reactor can be applied to the nuclear power field to obtain accurate target operating decisions, ensure the safe and stable operation of the nuclear reactor, improve the safety and safety management level of the nuclear reactor, and greatly reduce the labor cost.

[0158] In one embodiment, the weight of the utility variable is positively correlated with the degree of influence of the operating state parameter corresponding to the utility variable on the fault.

[0159] It can be understood that according to the importance of each operating state parameter in the nuclear reactor operating decision, the corresponding utility variable of the operating state parameter is given a corresponding weight. The importance can be judged according to the actual situation, and the weight can be set according to the actual situation, and no special limitation is made here.

[0160] The weight is used to measure the importance of the utility variable in the alternative operating decisions, and the choice of the weight will change the obtained target operating decision.

[0161] In this embodiment, the weight of the utility variable is positively correlated with the degree of influence of the operating state parameter corresponding to the utility variable on the fault, avoiding decision-making deviations caused by subjective biases or improper weight distribution, ensuring the safe and stable operation of the nuclear reactor, and improving the safety and safety management level of the nuclear reactor.

[0162] In one embodiment, step S103 includes: for each alternative operation decision, perform a weighted sum of the actual utility values of the utility variables, and calculate the composite utility of the alternative operation decision based on the weighted result. Determine the alternative operation decision with the maximum composite utility as the target operation decision.

[0163] When evaluating multiple alternative operation decisions, first calculate the actual utility value of each utility variable in each alternative operation decision, then multiply the actual utility value by the corresponding weight, and obtain the composite utility of the alternative operation decision through weighted summation to determine the target operation decision. Among them, the alternative operation decision with the maximum composite utility is the target operation decision.

[0164] See Figure 8 , the calculation formula of the composite utility is as follows:

[0165]

[0166] Among them, U represents the composite utility, N represents the total number of utility variables, ω i represents the weight corresponding to each utility variable, x i represents the i-th utility variable, u(x i ) represents the value of the dependent variable of the utility function, that is, the actual utility value of the utility variable.

[0167] In this embodiment, for each alternative operation decision, perform a weighted sum of the actual utility values of the utility variables, calculate the composite utility of the alternative operation decision based on the weighted result, and determine the unique target operation decision according to the composite utility, improving the safety and safety management level of the nuclear reactor and significantly reducing the labor cost.

[0168] In one embodiment, step S103 includes: for each alternative operation decision, perform a weighted sum of the actual utility values of the utility variables, and calculate the composite utility of the alternative operation decision based on the weighted result. Determine the alternative operation decision with the maximum composite utility as the target operation decision.

[0169] In this embodiment, for each alternative operation decision, perform a weighted sum of the actual utility values of the utility variables, calculate the composite utility of the alternative operation decision based on the weighted result, and determine the target operation decision according to the composite utility, improving the safety and safety management level of the nuclear reactor and significantly reducing the labor cost.

[0170] In one embodiment, introduce the "risk guidance" concept, couple and analyze the probabilistic decision-making process and the deterministic decision-making process. Each alternative operation decision is a sequence of a decision tree. The steps of calculating the composite utility of the alternative operation decision based on the weighted result include: determining the success probability of each alternative operation decision according to the decision tree. Multiply the success probability by the weighted result to obtain the composite utility of the alternative operation decision.

[0171] The goal of the deterministic decision analysis module is to integrate the current operating state parameters and the expected operating state parameters of the nuclear reactor and incorporate them into the decision-making process.

[0172] Probabilistic safety assessment technology (PSA) can achieve a probabilistic decision-making process. Compared with the deterministic decision-making process that only uses deterministic methods, probabilistic safety analysis technology can reveal the inherent risks of nuclear reactor operation within a wider range and provide more comprehensive and in-depth risk information. By applying probabilistic safety analysis technology, not only can uncertainties be quantified, but also comprehensive decisions guided by risks can be made, thereby further improving the safety and safety management level of nuclear power plants.

[0173] However, the probabilistic decision-making process simply selects the alternative operating decision with the highest success probability without considering other factors. For example, among the alternative operating decisions to avoid triggering a nuclear reactor shutdown, the one with the highest success probability is to manually shut down the reactor. However, if the changes in the operating state parameters of the nuclear reactor (such as temperature) are considered, this alternative operating decision may be determined as the most unfavorable one. In addition, the probabilistic decision-making process cannot explain the impact of the control actions in the alternative operating decisions on the operating state parameters. For example, implementing control actions will have effects such as reducing temperature and heat.

[0174] Because although the probabilistic decision analysis module automatically generates a corresponding set of control actions for each sequence, it does not specify the performance parameters of the components associated with the control actions. Moreover, some of the instructions generated by the probabilistic decision analysis module only contain abstract action concepts without specific explanations. For example, an instruction may be to reduce power, but it does not specify how much power needs to be reduced.

[0175] Specifically, referring to Figure 9 , through the probabilistic decision analysis module, a probabilistic decision analysis process is realized to obtain multiple alternative operating decisions. Each alternative operating decision is a sequence of the decision tree. Each alternative operating decision includes a varying number of control actions. Based on the component status and availability corresponding to the control actions, the success probability of each sequence of the decision tree is determined, that is, the success probability of each alternative operating decision is determined, and then the alternative operating decisions are screened based on the success probability. For example, if the component does not fail or its performance does not decline, the success probability is relatively high.

[0176] Figure 9 The decision node in is the start of the decision-making process. Alternative solutions 1-N are N alternative operating decisions. Actions 1-i, 1-j, 1-k, and 1-l respectively represent multiple control actions in each alternative operating decision. The success probabilities p1-p Nis the success probability of each alternative operation decision, and the probabilistic decision analysis module can output the success probability corresponding to each alternative operation decision.

[0177] After the alternative operation plans and their corresponding success probabilities are obtained in the probabilistic decision-making process, the alternative operation plans and success probabilities are transmitted to the deterministic decision analysis module. The deterministic decision-making process is implemented through the deterministic decision analysis module. Specifically, the operating state parameters are selected, and the corresponding relationship between the operating state parameters and the utility variables in multiple alternative operation decisions of the nuclear reactor is established, that is, the operating state parameters are linked to the utility variables to obtain the utility variables. Determine the weights corresponding to the utility variables, construct the utility function, and finally obtain the actual utility value according to the utility function. Calculate the composite utility based on the actual utility value and weight of the utility variable, and determine the target operation decision of the nuclear reactor from the multiple alternative operation plans obtained in the probabilistic decision-making process based on the composite utility.

[0178] Considering the success probabilities of each control behavior of the alternative operation plans, another calculation formula for the composite utility is obtained:

[0179]

[0180] Among them, U i represents the composite utility, p i represents the success probability of the i-th alternative operation decision, that is, the success probability of the i-th sequence, N represents the total number of utility variables, ω j represents the weight corresponding to each utility variable, x j represents the j-th utility variable, u j (x j ) represents the value of the dependent variable of the utility function after substituting the utility variable into the utility function, that is, the actual utility value of the utility variable.

[0181] In this embodiment, a comprehensive decision is made by combining the probability theory method and the determinism method to implement the probabilistic decision-making process and the deterministic decision-making process. It not only considers the defense in depth and safety margin, but also considers the success probability of the alternative operation decisions, makes risk guidance, integrates the risk awareness into the safety management mode of the nuclear reactor, improves the safety and safety management level of the nuclear reactor, greatly reduces the labor cost, and reduces the unnecessary burden.

[0182] In one embodiment, step S101 includes: screening out alternative operation decisions according to the success probability of each sequence of the decision tree; each sequence represents an operation decision; the success probability is related to the expected loss.

[0183] Based on the real-time status information of the nuclear power plant equipment, determine the success probability of each sequence according to the component status and availability of the components associated with the control actions, and sort the sequences. The probabilistic decision analysis module will automatically select alternative operating decisions as the best alternative operating decisions for implementing the corresponding corrective measures. The selected decision alternatives comprehensively consider the uncertainties of component status and availability in the fault state of the nuclear reactor. Selecting any one of the alternatives should prevent or minimize the possibility of activating the reactor protection system and keep the operating state parameters of the nuclear reactor within an acceptable range, so that the nuclear reactor can continue to operate normally.

[0184] In one embodiment, step S101 further includes: updating the success probability of the alternative operating decisions in the decision tree according to the system equipment status of the nuclear reactor monitored in real time.

[0185] In one embodiment, step S101 further includes: arranging the sequences in reverse order according to the success probability, and determining the operating decisions corresponding to the sequences with success probability greater than the probability threshold as the alternative operating decisions; or determining the operating decisions corresponding to the preset number of sequences ranked at the top as the alternative operating decisions.

[0186] The probability threshold can be set according to the actual situation and is not specifically limited here.

[0187] Adopting a risk-informed decision analysis method, that is, making a comprehensive decision by combining probability theory methods and deterministic methods has the following advantages: (1) comprehensively considering events that pose potential challenges to the nuclear power plant; (2) prioritizing these events that pose potential challenges to the nuclear power plant; (3) more widely considering and integrating various resources to prevent or mitigate potential risks and challenges; (4) clearly identifying and quantifying the sources of uncertainty; (5) providing a method for conducting sensitivity tests on the results of key assumptions to make better decisions.

[0188] In this embodiment, arranging the alternative operating decisions in reverse order according to the success probability and screening the alternative operating decisions with success probability greater than the probability threshold greatly reduces the labor cost and alleviates the unnecessary burden.

[0189] In one embodiment, after the step of determining the success probability of each alternative operating decision according to the decision tree, the risk-informed multi-attribute autonomous operating decision method for the nuclear reactor further includes: arranging the alternative operating decisions in reverse order according to the success probability, and only retaining the alternative operating decision with the highest success probability.

[0190] According to the real-time status information of the nuclear power plant equipment, determine the success probability based on the component status and availability of the components corresponding to the alternative operation decisions, and sort multiple alternative operation decisions. The probabilistic decision analysis module will automatically select the best candidate control option, that is, only retain the alternative operation decision with the highest success probability.

[0191] In this embodiment, the success probability of the alternative operation decision is considered to make a risk guidance, only retain the alternative operation plan with the highest success probability, integrate the risk awareness into the safety management mode of the nuclear reactor, improve the safety and safety management level of the nuclear reactor, greatly reduce the labor cost, minimize the possibility of starting the nuclear reactor protection system, and keep the operating state parameters of the nuclear reactor within the safe range, so that the nuclear reactor can continue to operate normally.

[0192] Next, the fault scenario of the steam turbine control valve is selected to verify the effectiveness of the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method.

[0193] The steam turbine control valve is an important control component in the nuclear reactor, and the opening of the steam turbine control valve directly affects the steam flow of the steam turbine. When the opening of the steam turbine control valve decreases and moves in the closing direction, it will cause the steam flow of the steam turbine to decrease, which in turn affects the cooling flow of the steam generator. The opening of the feed water flow control valve increases, making the cooling flow of the steam generator increase, which will cause subcooling of the primary loop system; conversely, the opening of the feed water flow control valve decreases, making the cooling flow of the steam generator decrease, which will cause undercooling of the primary loop system. If the steam flow cannot be increased or the feed water flow cannot be reduced, it will lead to heat imbalance in the secondary cooling system and nuclear reactor shutdown.

[0194] After determining the fault scenario, use the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method to determine the target operation decision.

[0195] The alternative operation plans include but are not limited to: the steam turbine control valve moves in the closing direction, the steam turbine control valve is reset, the steam generator bypass valve is in the open state, adjusting the steam turbine bypass valve or the feed water flow, reducing the feed water flow to the steam generator, opening the steam turbine bypass valve, reducing the power of the nuclear reactor, closing the steam turbine bypass valve, and manually shutting down the nuclear reactor, etc. Specific selections can be made according to the actual situation and are not particularly limited here.

[0196] Screen the alternative operation plans to obtain the following 5 alternative operation plans:

[0197] 1) The nuclear reactor shuts down at low water level in the steam generator, that is, no measures are taken;

[0198] 2) The steam turbine control valve is reset to restore it to the normal working position;

[0199] 3) Open the steam turbine bypass valve for short-term compensation; at the same time, it is recommended to reduce the power of the nuclear reactor or repair the failure of the steam turbine control valve.

[0200] 4) If the power of the nuclear reactor does not reach 100%, open the steam generator bypass valve of the nuclear reactor; at the same time, it is recommended to manually shut down another nuclear reactor or repair the failure of the steam turbine control valve;

[0201] 5) Reduce the feed water flow rate to the steam generator; at the same time, it is recommended to reduce the power of the nuclear reactor or repair the failure of the steam turbine control valve.

[0202] The above five alternative operation decisions are the five sequences of the decision tree. Determine the success probability of each alternative operation decision according to the decision tree, and arrange the alternative operation decisions according to the success probability. The arrangement after sorting is shown in Table 4.

[0203] Table 4 Control options sorted according to the likelihood of success

[0204]

[0205] Among them, the likelihood of success is the success probability, the control option is the alternative operation decision, and the reactor module 1 is a nuclear reactor. The first row of Table 4 corresponds to the first of the 5 alternative operation decisions, that is, taking no measures. The second row of Table 4 corresponds to the fourth of the 5 alternative operation decisions, that is, manually shutting down another nuclear reactor or repairing the failure of the steam turbine control valve. The third row of Table 4 corresponds to the fifth of the 5 alternative operation decisions, that is, reducing the feed water flow rate to the steam generator; at the same time, it is recommended to reduce the power of the nuclear reactor or repair the failure of the steam turbine control valve. The fourth row of Table 4 corresponds to the third of the 5 alternative operation decisions, that is, opening the steam turbine bypass valve for short-term compensation. The fifth row of Table 4 corresponds to the second of the 5 alternative operation decisions, that is, resetting the steam turbine control valve to restore it to the normal working position.

[0206] It can be seen that although the success probability of the first alternative operation decision is very high, the result is an emergency reactor shutdown, which means that the nuclear reactor will immediately stop operating. Although this can avoid potential safety risks, it will also bring significant economic losses and production interruptions at the same time. Therefore, although the success probability of the first alternative operation decision is high, it is usually not the first choice in practical applications. The second alternative operation decision is to manually shut down the nuclear reactor. Although it helps to stabilize the system, it will cause the power of the nuclear reactor to drop to 50%, which means that the power generation capacity of the nuclear reactor will decrease significantly. In addition, shutting down a nuclear reactor may also affect the load balance and stability of the entire nuclear power plant. Although the success probability of the fifth alternative operation decision is low, once it succeeds, the nuclear reactor will be able to resume operation at 100% power. However, due to the very low success probability, the risk is relatively high in practical applications.

[0207] Both the third alternative operation decision and the fourth alternative operation decision have relatively high success probabilities, and the results are both power reductions. Although the specific degree of power reduction is not given, both the third alternative operation decision and the fourth alternative operation decision attempt to balance heat and flow by adjusting operation parameters, so as to maintain the stability of the nuclear reactor. Compared with an emergency reactor shutdown or a significant power reduction, the third alternative operation decision and the fourth alternative operation decision are more moderate and avoid directly bringing significant economic losses. Since the results of the third alternative operation decision and the fourth alternative operation decision are relatively abstract and not specifically described. Therefore, in order to more accurately evaluate the advantages and disadvantages of the third alternative operation decision and the fourth alternative operation decision, the third alternative operation decision and the fourth alternative operation decision are selected and transmitted to the deterministic decision analysis module for analysis to obtain an accurate target operation decision.

[0208] Through the input of variable sensors, the changes in the operation state parameters after the execution of the two alternative operation decisions can be obtained. In this embodiment, the operation state parameters include but are not limited to the following four: the core outlet temperature, the main loop cold pool temperature, the steam header pressure, and the steam drum level. The operation state parameters can be selected according to the actual situation and are not specifically limited here. The operation state parameters can usually be used to characterize the physical behavior of the nuclear reactor or to predict whether the nuclear reactor will malfunction in the future.

[0209] Taking the third alternative operation decision as an example, after the execution of the third alternative operation decision, the change of the core outlet temperature over time is as Figure 10 shown. Among them, reactor module 1 and reactor module 2 are two nuclear reactors respectively. The horizontal axis represents time, and the vertical axis represents the core outlet temperature. After the execution of the third alternative operation decision, the change of the utility value corresponding to the core outlet temperature is as Figure 11As shown in the figure. Among them, the boundary points of the safety range are marked by red circles, and the boundary points can be used to determine whether the nuclear reactor has shut down, which can be set according to the actual situation and will not be specifically limited here. The actual utility value of the core outlet temperature of the reactor module 2 is positive, while the actual utility value of the core outlet temperature of the reactor module 1 is negative. The closer to the boundary value, the actual utility value of the utility variable approaches 0, and the decreasing amplitude of the actual utility value will increase rapidly. For the nuclear reactor control system, a negative actual utility value indicates that this alternative operating decision cannot be the target operating decision.

[0210] Table 5 lists the parameter values of the operating state parameters of the nuclear reactor for the 3rd and 4th alternative operating decisions. Among them, RX1 represents the first nuclear reactor, that is, the reactor module 1; RX2 represents the second nuclear reactor, that is, the reactor module 2; control option 3 represents the 3rd alternative operating decision; control option 4 represents the 4th alternative operating decision.

[0211] Table 5 Attribute values of key process variables of reactor modules for control option 3 and control option 4

[0212]

[0213] Establish the correspondence between the operating state parameters of the nuclear reactor and the utility variables. Specifically, adopt the standard 0-1 transformation to convert the operating state parameters into utility variables, and the obtained utility variables are shown in Table 6.

[0214] Table 6 Utility variable matrix

[0215]

[0216] For each alternative operating decision, obtain the weight corresponding to the utility variable and construct a utility function for the operating state parameters. Specifically, obtain the utility functions corresponding to each utility variable through affine transformation, and the transformation coefficients a and b of the affine transformation are shown in Table 7.

[0217] Table 7 Transformation coefficients a and b corresponding to the utility functions of each utility variable

[0218]

[0219] The utility functions of each utility variable are as Figure 12 shown. The utility function represents the correspondence between the parameter value of the operating state parameter and the actual utility value of the utility variable. The actual utility value of the utility variable corresponding to the operating state parameter whose parameter value does not exceed the safety range is greater than the actual utility value of the utility variable corresponding to the operating state parameter whose parameter value exceeds the safety range.

[0220] Calculate the actual utility values of the utility variables corresponding to multiple alternative operating decisions.

[0221] Table 8 lists the actual utility values of the utility variables, the reactor module, and the total plant utility value, and calculates the composite utility based on the success probability and the actual utility value. Among them, the likelihood of success is the success probability.

[0222] Table 8 Utility values of control options 3 and 4

[0223]

[0224] The actual utility values of the utility variables are weighted and summed, and the composite utility of the alternative operating decisions is calculated based on the weighted results. The success probability is multiplied by the weighted results to obtain the composite utility of the alternative operating decisions.

[0225] In this embodiment, the risk-guided multi-attribute autonomous operation decision-making method for nuclear reactors has been effectively applied. Although the success probability of the third alternative operating decision is slightly higher than that of the fourth alternative operating decision, the composite utility of the fourth alternative operating decision is significantly higher than that of the third alternative operating decision. The fourth alternative operating decision is a more ideal decision-making scheme, achieving the goals of risk minimization and performance optimization. The risk-guided multi-attribute autonomous operation decision-making method for nuclear reactors is of great significance for guiding the operation and maintenance in the actual nuclear power field and also provides a useful reference for future similar decision-making problems.

[0226] The research results of this study are practical in the field of nuclear reactor autonomous operation decision-making, providing new ideas and methods for improving the quality and efficiency of engineering decision-making.

[0227] This embodiment introduces utility theory into the engineering field. This attempt not only broadens the application scope of utility theory but also provides a new perspective for solving decision-making problems in the engineering field. Utility theory was initially mainly applied in the field of economics to describe and predict the decision-making behavior of individuals when facing multiple choices. By constructing a utility function, this theory can quantify the satisfaction or utility brought by different choices to individuals, thus helping individuals make optimal decisions. In economics, the application of utility theory has been quite mature, providing strong support for resource allocation, market analysis, and policy formulation. However, in the engineering field, the decision-making problems of large and complex control systems often involve multiple interrelated factors and complex constraints, making it difficult for traditional optimization methods to cope. This embodiment introduces utility theory into the engineering field, aiming to construct a utility function applicable to engineering problems. The risk-guided multi-attribute autonomous operation decision-making method for nuclear reactors makes the decision-making results more in line with the actual needs and safety standards of nuclear power plants, not only overcoming the limitations of traditional methods but also making the solutions to engineering problems more in line with the actual situation and the needs of decision-makers.

[0228] Specifically, first, it broadens the application scope of the utility theory, introducing it from the field of economics to the engineering field, providing new theoretical support for solving engineering problems and expanding the definition of the utility function; second, by constructing a utility function and decision analysis method applicable to engineering problems, it improves the accuracy and effectiveness of engineering problem solutions; third, it provides a more comprehensive and flexible decision-making tool for decision-makers, helping to improve the scientific and rational nature of decision-making.

[0229] In this embodiment, the probabilistic decision analysis module is combined with the deterministic decision analysis module, giving full play to the advantages of both. By selecting the fault scenario where the opening of the steam turbine control valve decreases and moves towards closing as a case study, the effectiveness of the proposed method in practical engineering problems is verified.

[0230] In summary, the risk - guided multi - attribute autonomous operation decision - making method for nuclear reactors not only enriches the application of multi - attribute utility theory in the field of engineering decision - making, but also provides new ideas and methods for risk - guided comprehensive decision - making analysis methods. It has innovation and practicality in the engineering field, and is of great significance for improving the quality and efficiency of engineering decision - making.

[0231] Embodiment 2

[0232] Corresponding to the foregoing embodiment of the risk - guided multi - attribute autonomous operation decision - making method for nuclear reactors, the present disclosure also provides an embodiment of a risk - guided multi - attribute autonomous operation decision - making system for nuclear reactors.

[0233] Figure 13 The figure is a schematic diagram of the modules of a risk - guided multi - attribute autonomous operation decision - making system for nuclear reactors provided by an exemplary embodiment of the present disclosure. The risk - guided multi - attribute autonomous operation decision - making system for nuclear reactors includes:

[0234] A probabilistic decision analysis module 201, configured to establish multiple alternative operation decisions for the nuclear reactor. The alternative operation decisions include the expected losses of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected losses meet the preset loss conditions;

[0235] A deterministic decision analysis module 202, configured to simulate the nuclear reactor based on each alternative operation decision to obtain the expected parameter values of the operation state parameters of the nuclear reactor and the expected utility values of the corresponding effect variables during the simulation process, and substitute the corresponding expected utility values into the utility function to calculate the actual utility values corresponding to each utility variable; wherein, the utility function represents the corresponding relationship between the operation state parameters and the utility variables, and the actual utility values of the utility variables corresponding to the state parameters whose parameter values do not exceed the safety range are greater than the actual utility values of the utility variables corresponding to the state parameters whose parameter values exceed the safety range;

[0236] The optimal decision-making generation module 203 is configured to determine the target operation decision of the nuclear reactor from multiple alternative operation decisions based on the actual utility value.

[0237] Among them, the optimal decision is the target operation decision.

[0238] Optionally, the utility function is obtained through the following steps: for each operation state parameter, an affine transformation is performed on the probability density distribution function of the operation state parameter to obtain the utility function.

[0239] Optionally, the probability density distribution function is represented by a Gaussian distribution.

[0240] Optionally, the probabilistic decision-making analysis module 201 includes:

[0241] A decision screening unit for screening out the alternative operation decisions according to the success probability of each sequence of the decision tree; each sequence represents an operation decision; the success probability is related to the expected loss;

[0242] And / or, a success probability updating unit for updating the success probability of the alternative operation decisions in the decision tree according to the system device state of the nuclear reactor monitored in real time;

[0243] And / or, a sorting unit for inversely sorting the sequences in reverse order according to the success probability, and determining the operation decisions corresponding to the sequences with a success probability greater than the probability threshold as the alternative operation decisions;

[0244] Or, the sorting unit is configured to determine the operation decisions corresponding to a preset number of sequences with a higher sorting as the alternative operation decisions.

[0245] Optionally, the optimal decision-making generation module 203 includes:

[0246] A weight determination unit for determining the weights corresponding to the utility variables;

[0247] A weighted summation unit for, for each alternative operation decision, performing a weighted summation on the actual utility values of the utility variables and calculating the composite utility of the alternative operation decision based on the weighted result;

[0248] A target operation decision determination unit for determining the alternative operation decision with the maximum composite utility as the target operation decision.

[0249] Optionally, the weighted summation unit is specifically configured to: determine the success probability of each alternative operation decision according to the decision tree; wherein, each alternative operation decision is a sequence of the decision tree; multiply the success probability by the weighted result to obtain the composite utility of the alternative operation decision.

[0250] In this embodiment, the multi-attribute utility theory algorithm is used as a mathematical analysis tool, which can comprehensively consider multiple operating state parameters. By establishing a unified scale to measure the value of multiple alternative operating decisions or the satisfaction of decision-makers with multiple alternative operating decisions, cross-standard comparison and decision-making are realized, and the actual utility value of the utility variable is calculated. By quantifying the impact of different alternative operating decisions on the nuclear reactor with the actual utility value, an accurate target operating decision is obtained, which can make quick and accurate decisions in a complex and changeable environment to ensure the safe and stable operation of the nuclear reactor, improve the safety and safety management level of the nuclear reactor, greatly reduce the labor cost, improve the scientificity and accuracy of decision-making, reduce decision-making risks, and provide strong support for engineering practice.

[0251] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative. The units described as separate components may or may not be physically separated. The components as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution.

[0252] Embodiment 3

[0253] Figure 14 It is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored on the memory and used to run on the processor. When the processor executes the computer program, it implements the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method described in any of the above embodiments. Figure 14 The displayed electronic device 30 is only an example and should not bring any restrictions to the functions and usage scopes of the embodiments of the present disclosure.

[0254] As Figure 14 shown, the electronic device 30 can be presented in the form of a general computing device, for example, it can be a server device. The components of the electronic device 30 may include, but are not limited to: at least one of the above processors 31, at least one of the above memories 32, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

[0255] The bus 33 includes a data bus, an address bus, and a control bus.

[0256] The memory 32 may include volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322, and may further include a read-only memory (ROM) 323.

[0257] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) of program modules 324. Such program modules 324 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each of these examples or some combination thereof may include the implementation of a network environment.

[0258] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method provided in any of the above embodiments.

[0259] The electronic device 30 may also communicate with one or more external devices 34 (such as a keyboard, a pointing device, etc.). Such communication may be performed through the input / output (I / O) interface 35. And, the electronic device 30 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 36. As shown in the figure, the network adapter 36 communicates with other modules of the electronic device 30 through the bus 33. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (redundant array of independent disks) systems, tape drives, and data backup storage systems, etc.

[0260] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described units / modules may be embodied in one unit / modules. Conversely, the features and functions of one unit / modules described above may be further divided and embodied by multiple unit / modules.

[0261] Embodiment 4

[0262] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the nuclear reactor risk-guided multi-attribute autonomous operation decision-making method provided in any of the above embodiments.

[0263] Among them, the more specific forms that the readable storage medium may adopt may include, but are not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0264] Although the specific embodiments of the present disclosure have been described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Without departing from the principles and essence of the present disclosure, those skilled in the art can make various changes or modifications to these embodiments, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A risk-guidance-based multi-attribute autonomous operation decision-making method for nuclear reactors, characterized in that The nuclear reactor risk - guided multi - attribute autonomous operation decision - making method includes: Establishing multiple alternative operation decisions for the nuclear reactor, where the alternative operation decisions include the expected losses of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected losses meet the preset loss conditions; Simulating the nuclear reactor based on each alternative operation decision to obtain the expected parameter values of the operation state parameters and the expected utility values of the corresponding effect variables during the simulation process, and substituting the corresponding expected utility values into the utility function to calculate the actual utility values corresponding to each utility variable; wherein, the utility function represents the corresponding relationship between the operation state parameters and the utility variables, and the actual utility values of the utility variables corresponding to the state parameters whose parameter values do not exceed the safety range are greater than the actual utility values of the utility variables corresponding to the state parameters whose parameter values exceed the safety range; Based on the actual utility values, determining the target operation decision of the nuclear reactor from multiple alternative operation decisions.

2. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making method according to claim 1, characterized in that The utility function is obtained through the following steps: For each of the operation state parameters, an affine transformation is performed on the probability density distribution function of the operation state parameter to obtain the utility function.

3. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making method according to claim 2, wherein, The probability density distribution function is represented by a Gaussian distribution.

4. The nuclear reactor risk-guidance multi-attribute autonomous operation decision-making method according to claim 1, characterized in that The step of establishing multiple alternative operation decisions for the nuclear reactor includes: Selecting the alternative operation decisions according to the success probability of each sequence of the decision tree; each sequence represents an operation decision; the success probability is related to the expected loss; And / or, updating the success probability of the alternative operation decisions in the decision tree according to the system equipment state of the nuclear reactor monitored in real time; and / or, arranging the sequences in reverse order according to the success probability, and determining the operation decisions corresponding to the sequences with success probabilities greater than the probability threshold as the alternative operation decisions; or, determining the operation decisions corresponding to the preset number of sequences ranked at the front as the alternative operation decisions.

5. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making method according to claim 4, characterized in that The step of determining the target operation decision of the nuclear reactor from multiple alternative operation decisions based on the actual utility values includes: Determining the weights corresponding to the utility variables; For each of the alternative operation decisions, weighted - summing the actual utility values of the utility variables according to the weights, and calculating the composite utility of the alternative operation decision based on the weighted result; Determining the alternative operation decision with the maximum composite utility as the target operation decision.

6. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making method according to claim 5, wherein, The step of calculating the composite utility of the alternative operation decision based on the weighted result includes: Multiplying the success probability by the weighted result to obtain the composite utility of the alternative operation decision.

7. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making method according to any one of claims 1-6, characterized in that The effect variable is obtained through the following formula: where x i is the i-th utility variable, and p i is the parameter value of the i-th operating state parameter; (p i ) max is the maximum value of the i-th operating state parameter, and (p i ) min is the minimum value of the i-th operating state parameter; And / or, the weight conforms to the following formula: where N represents the total number of said utility variables, and ω j represents the weight corresponding to the j-th said utility variable; And / or, the expression of the probability density distribution function is as follows: Wherein, p(x|μ,σ) represents the probability density distribution function, μ represents the mean of the probability density distribution, σ represents the standard deviation of the probability density distribution, and e represents the base of the natural logarithm; And / or, the expression of the utility function is as follows: where u(x|μ,σ) represents the utility function, and a and b are transformation coefficients of the affine transformation; the transformation coefficients a and b of the affine transformation are calculated by the following formula: a + b = 1 and / or, the composite utility is obtained by the following formula: Among them, U represents the composite utility, and u(x i ) represents the actual utility value of the utility variable.

8. A risk-guidance multi-attribute autonomous operation decision-making system for a nuclear reactor, characterized in that, The nuclear reactor risk - guided multi - attribute autonomous operation decision - making system includes: A probabilistic decision - making analysis module, configured to establish multiple alternative operation decisions for the nuclear reactor. The alternative operation decisions include the expected loss of the nuclear reactor after executing the control actions included in the alternative operation decisions, and the expected loss meets the preset loss condition; A deterministic decision - making analysis module, configured to simulate the nuclear reactor based on each alternative operation decision to obtain the expected parameter values of the operation state parameters and the expected utility values of the corresponding effect variables during the simulation process, and substitute the corresponding expected utility values into the utility function to calculate the actual utility values corresponding to each utility variable. Wherein, the utility function represents the corresponding relationship between the operation state parameters and the utility variables, and the actual utility value of the utility variable corresponding to the state parameter whose parameter value does not exceed the safety range is greater than the actual utility value of the utility variable corresponding to the state parameter whose parameter value exceeds the safety range; An optimal decision - making generation module, configured to determine the target operation decision of the nuclear reactor from multiple alternative operation decisions based on the actual utility values.

9. The nuclear reactor risk-guided multi-attribute autonomous operation decision-making system according to claim 8, wherein The effect variable is obtained by the following formula: where x i is the i-th utility variable, and p i is the parameter value of the i-th operating state parameter; (p i ) max is the maximum value of the i-th operating state parameter, and (p i ) min is the minimum value of the i-th operating state parameter; and / or, the weight conforms to the following formula: where N represents the total number of said utility variables, and ω j represents the weight corresponding to the j-th said utility variable; and / or, the expression of the probability density distribution function is as follows: where p(x|μ,σ) represents the probability density distribution function, μ represents the mean of the probability density distribution, σ represents the standard deviation of the probability density distribution, and e represents the base of the natural logarithm; and / or, the expression of the utility function is as follows: where u(x|μ,σ) represents the utility function, and a and b are transformation coefficients of the affine transformation; the transformation coefficients a and b of the affine transformation are calculated by the following formula: a + b = 1 and / or, the composite utility is obtained by the following formula: where U represents the composite utility, and u(x i ) represents the actual utility value of the utility variable.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the nuclear reactor risk - guided multi - attribute autonomous operation decision - making method according to any one of claims 1 to 6.

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