Design method of variable parameter grading and early warning for intelligent operation monitoring system of nuclear power plant
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NUCLEAR POWER ENGINEERING CO LTD
- Filing Date
- 2026-02-05
- Publication Date
- 2026-06-02
Smart Images
Figure CN122134112A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of nuclear power safety technology, and in particular to a design method for classifying and early warning variable parameters in an intelligent operation monitoring system for nuclear power plants. Background Technology
[0002] Nuclear power, as a green, stable, and clean energy source, is characterized by safety and economy, which are also the cornerstones of its survival and development. A healthy unit condition is a prerequisite for ensuring the safety, stability, and economic efficiency of nuclear power. This is achieved by monitoring the variable status of nuclear power equipment; that is, the real-time measurement values of these variables characterize whether the unit is currently in a healthy state. Real-time monitoring of these variables is not only a requirement of the first level of nuclear safety defense-in-depth, but also a key daily task for operators in the control room.
[0003] In accordance with the technical requirements and management regulations for the safe and economical operation of nuclear power plants, and based on the established scope and frequency, control room operators periodically monitor the real-time status of various variables by browsing the screens on the digital instrumentation and control system set up on the control panel. This is to identify early anomalies in the equipment and intervene in a timely manner to prevent further deterioration and equipment failure, which would ultimately affect the safety and economy of the unit. This work is commonly referred to as panel monitoring. Generally, control room operators monitor variables at a certain frequency (intervals vary from every 2 hours to every 4 hours) and identify and judge possible anomalies based on their own knowledge and operational experience (commonly referred to as manual panel monitoring). However, the main disadvantages of manual panel monitoring include: discontinuous monitoring; large workload and high intensity; high skill requirements for personnel; difficulty in identifying early anomalies; and complex intervention and handling methods.
[0004] Existing technology CN116166981A discloses a method for classifying nuclear power plant equipment, including: S1, constructing a nuclear power plant equipment classification system, matching nuclear power plant equipment classification categories with specific functional issues; classifying nuclear power plant equipment according to its functions, categorizing identified problems in nuclear power plant equipment, and performing parameter matching to form a nuclear power plant equipment classification system structure; S2, extracting the functions of identified equipment in the nuclear power plant, and transforming variables according to the equipment functional status; extracting the specific functional content of each classification category in the nuclear power plant equipment classification system, and matching it with variable parameters; S3, classifying nuclear power plant equipment according to the variable combination of equipment functional status. This scheme focuses on equipment-level classification and lacks refined management of specific variable parameters in the equipment, meaning it cannot fully identify and respond to variable changes that have a relatively small impact on safety and economy but may have significant consequences when accumulated in large quantities, especially when dealing with tens of thousands of parameters in the intelligent operation monitoring system of a nuclear power plant.
[0005] Existing technology CN106204324A discloses a method for determining key monitoring parameters and assigning weights to complex equipment in a power plant. This method utilizes historical data from the power plant's SIS system under normal operating conditions of the complex equipment. Principal component analysis is performed on all monitoring parameters to obtain the contribution rate of each parameter, thereby identifying the key monitoring parameters and assigning weights to them. However, this approach fails to fully consider the unique attributes and safety requirements of nuclear power equipment, potentially overlooking certain qualitative or semi-quantitative safety assessment principles in nuclear safety. Furthermore, it lacks direct guidance for early warning strategies, only providing methods for parameter selection and weight allocation without clearly defining how to translate these key parameters into concrete early warning actions. Summary of the Invention
[0006] Intelligent operation monitoring systems in nuclear power plants can automatically and synchronously monitor thousands of unit variable parameters. When individual or partial variable parameters become abnormal, the system automatically triggers an alert to remind operators to pay attention or intervene. However, the number of unit variable parameters is enormous, and they vary in importance, the impact of anomalies on unit performance, and the required intervention measures. Therefore, it is urgent to conduct systematic analysis and research on the pre-defined principles for classifying these numerous variable parameters and developing diverse early warning strategies.
[0007] The purpose of this application is to solve the aforementioned technical problems.
[0008] To achieve the above objectives, the first aspect of this application proposes a design method for classifying and issuing early warnings of variable parameters in an intelligent operation monitoring system for nuclear power plants, including: Obtain all variable parameters and their measured values from the intelligent operation monitoring system of a nuclear power plant; All variable parameters are classified into levels based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of the safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet any of them are non-core variable parameters. For core variable parameters, thresholds are generated using the comparison method, residual method, and empirical method, respectively, and early warnings are issued based on the comparison results between the measured values of the core variable parameters and the thresholds. For non-core variable parameters, thresholds are generated using both comparative and empirical methods, and warnings are issued based on the comparison between the measured values of the non-core variable parameters and the thresholds.
[0009] Furthermore, for the core variable parameters, thresholds are generated using the comparison method, residual method, and empirical method, respectively. Specifically, for the core variable parameters, a first distribution threshold, a residual threshold, and a first empirical threshold are generated using the comparison method, residual method, and empirical method, respectively.
[0010] Furthermore, the system provides early warnings based on the comparison between the measured values of the core variable parameters and the thresholds. This includes using the first distribution threshold, the residual threshold, and the first empirical threshold as judgment values; issuing a warning signal of concern when the measured value of the core variable parameter exceeds any one of the judgment values; and issuing an intervention warning signal when the measured value of the core variable parameter exceeds any two of the judgment values.
[0011] Furthermore, for non-core variable parameters, thresholds are generated using both comparative and empirical methods, including generating a second distribution threshold and a second empirical threshold using both comparative and empirical methods.
[0012] Furthermore, the warning is based on the comparison between the measured values of non-core variable parameters and the thresholds, including using the second distribution threshold and the second empirical threshold as the judgment values; issuing a warning signal of concern when the measured value of a non-core variable parameter exceeds any one of the judgment values; and issuing an intervention warning signal when the measured value of a non-core variable parameter exceeds two of the judgment values simultaneously.
[0013] Furthermore, the safety evaluation indicators include logic operation variables used to stop reactor operation, variables used to characterize operating limitations and conditions under various operating conditions of the unit, variables used to characterize monitoring requirements under various operating conditions of the unit, variables used for periodic inspections of the primary and secondary circuit main control rooms, and variables whose safety risk probability exceeds the preset limit.
[0014] Furthermore, the economic evaluation indicators include logical operation type variable parameters used to stop the operation of the steam turbine, variable parameters that would cause the unit to immediately stop generating electricity due to an anomaly, variable parameters that would cause the unit to stop generating electricity for more than or equal to 3 days due to an anomaly, variable parameters that would cause the unit to reduce its power output to less than or equal to 90% of its rated power due to an anomaly, and variable parameters that would result in an economic risk probability higher than a preset limit.
[0015] To achieve the above objectives, the second aspect of this application proposes a design device for classifying and issuing early warnings of variable parameters in a nuclear power plant intelligent operation monitoring system, comprising: The acquisition module is used to acquire all variable parameters and their measured values in the intelligent operation monitoring system of the nuclear power plant; The grading module is used to classify all variable parameters based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of the safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet either are non-core variable parameters. The core variable parameter early warning module is used to generate thresholds for core variable parameters using the comparison method, residual method and empirical method respectively, and to issue early warnings based on the comparison results between the measured values of the core variable parameters and the thresholds. The non-core variable parameter early warning module is used to generate thresholds for non-core variable parameters using both comparative and empirical methods, and to issue early warnings based on the comparison results between the measured values of the non-core variable parameters and the thresholds.
[0016] To achieve the above objectives, a third aspect of this application proposes a computer-readable storage medium comprising a stored computer program, wherein the computer program can be executed by an electronic device to provide the design method for classifying and issuing early warnings of variable parameters in a nuclear power plant intelligent operation monitoring system.
[0017] To achieve the above objectives, the fourth aspect of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the design method for classifying and issuing early warnings of variable parameters in a nuclear power plant intelligent operation monitoring system.
[0018] To achieve the above objectives, the fifth aspect of this application proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute, through the computer program, the design method for classifying and warning variable parameters of a nuclear power plant intelligent operation monitoring system.
[0019] By applying the above-described technical solution of the present invention, at least the following technical effects are achieved: 1. This invention combines variable parameter classification with customized early warning strategies to achieve efficient and accurate management of tens of thousands of variable parameters in the intelligent operation monitoring system of nuclear power plants, which greatly improves the safety and economy of nuclear power operation. Through automated identification and early warning, it reduces the burden of manual monitoring and the risk of misjudgment, enhances the system's sensitivity to early anomalies and response speed, and provides strong support for the intelligent operation of nuclear power units. 2. By introducing identification methods such as comparison method, residual method and empirical method, this invention can automatically monitor and classify variable parameters, reduce the manual monitoring tasks of the main control room operator, improve the accuracy and timeliness of anomaly detection, and reduce the risk of human error. 3. By establishing a set of core variable parameters and a set of non-core variable parameters, and by formulating differentiated early warning strategies, this invention can more effectively identify early abnormal signals and notify operators in a timely manner, even in complex and dynamic nuclear power operating environments, thus preventing the escalation of potential accidents. 4. By classifying variable parameters according to their importance to nuclear power safety and economy, this invention can more rationally allocate limited monitoring resources and technical support, ensuring real-time monitoring and rapid response of key parameters.
[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0021] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart of a design method for variable parameter classification and early warning in an intelligent operation monitoring system for nuclear power plants, according to one embodiment, is presented; Figure 2 A schematic diagram of variable parameter grading in one embodiment is shown; Figure 3 A schematic diagram of the threshold value of the variable parameter in one embodiment is shown; Figure 4 A detailed flowchart of a design method for variable parameter classification and early warning in an intelligent operation monitoring system for nuclear power plants, according to one embodiment, is presented. Figure 5 A schematic diagram of the structure of a design device for classifying and early warning variable parameters in an intelligent operation monitoring system for nuclear power plants, according to one embodiment, is provided. Figure 6 A schematic diagram of the structure of a design product for a nuclear power plant intelligent operation monitoring system with variable parameter classification and early warning, according to one embodiment, is presented; Figure 7 A schematic diagram of the structure of an electronic device according to an embodiment is shown. Detailed Implementation
[0022] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0023] The present invention will be further described in detail below with reference to specific embodiments, which should not be construed as limiting the scope of protection claimed by the present invention.
[0024] Example 1
[0025] According to one aspect of the present invention, a design method for classifying and early warning of variable parameters in an intelligent operation monitoring system for nuclear power plants is proposed.
[0026] like Figure 1 The present invention illustrates a design method for variable parameter classification and early warning in a nuclear power plant intelligent operation monitoring system according to an embodiment of the present invention. The process mainly includes the following steps: S1. Obtain all variable parameters and their measured values from the intelligent operation monitoring system of the nuclear power plant.
[0027] S2. All variable parameters are classified based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet either are non-core variable parameters.
[0028] The purpose of classifying variable parameters is to classify all variable parameters (variable parameter set) included in the intelligent operation monitoring system according to preset safety evaluation indicators and economic evaluation indicators, forming a core variable parameter set and a non-core variable parameter set, laying the foundation for subsequent targeted development of diversified early warning strategies.
[0029] Furthermore, the safety evaluation indicators include logic operation variables used to stop reactor operation, variables used to characterize operating limitations and conditions under various operating conditions of the unit, variables used to characterize monitoring requirements under various operating conditions of the unit, variables used for periodic inspections of the primary and secondary circuit main control rooms, and variables whose safety risk probability exceeds the preset limit.
[0030] Furthermore, the economic evaluation indicators include logical operation type variable parameters used to stop the operation of the steam turbine, variable parameters that would cause the unit to immediately stop generating electricity due to an anomaly, variable parameters that would cause the unit to stop generating electricity for more than or equal to 3 days due to an anomaly, variable parameters that would cause the unit to reduce its power output to less than or equal to 90% of its rated power due to an anomaly, and variable parameters that would result in an economic risk probability higher than a preset limit.
[0031] Specifically, taking the containment atmosphere monitoring system (CAM) in a certain type of intelligent operation monitoring system as an example, the classification of its variable parameters is as follows: Figure 2 As shown.
[0032] S3. For core variable parameters, thresholds are generated using the comparison method, residual method, and empirical method, respectively, and early warnings are issued based on the comparison results between the measured values of the core variable parameters and the thresholds.
[0033] Anomaly warnings employ diverse strategies, using combined thresholds generated by multiple anomaly identification methods as judgment values for various types of variable parameter sets. Warnings are issued when the measured values of variable parameters meet the judgment criteria.
[0034] Furthermore, for the core variable parameters, the first distribution threshold, residual threshold, and first empirical threshold are generated using the comparison method, residual method, and empirical method, respectively.
[0035] Furthermore, the first distribution threshold, residual threshold, and first empirical threshold are used as judgment values; when the measured value of the core variable parameter exceeds any one of the judgment values, an alert signal for attention is issued; when the measured value of the core variable parameter exceeds any two of the judgment values, an alert signal for intervention is issued.
[0036] Specifically, in this embodiment, for the core variable parameter, anomaly identification is composed of a combination of three methods: comparison method, residual method, and empirical method. The comparison method forms a first distribution threshold, the residual method forms a dynamic threshold, and the empirical method forms a first empirical threshold. The first distribution threshold, residual threshold, and first empirical threshold are used as judgment values. When the measured value of the variable parameter exceeds any one of the three, attention is required; when the measured value of the variable parameter exceeds any two of the three, intervention is necessary.
[0037] S4. For non-core variable parameters, thresholds are generated using both comparative and empirical methods, and warnings are issued based on the comparison between the measured values of the non-core variable parameters and the thresholds.
[0038] Furthermore, for non-core variable parameters, a second distribution threshold and a second empirical threshold are generated using the comparison method and the empirical method, respectively.
[0039] Furthermore, the second distribution threshold and the second empirical threshold are used as judgment values; when the measured value of a non-core variable parameter exceeds any one of the judgment values, an alert signal for attention is issued; when the measured value of a non-core variable parameter exceeds two of the judgment values simultaneously, an alert signal for intervention is issued.
[0040] Specifically, in this embodiment, for non-core variable parameters, anomaly identification is achieved through a combination of comparative and empirical methods. The comparative method forms a first distribution threshold, and the empirical method forms a first empirical threshold. The first distribution threshold and the first empirical threshold are used as judgment values. When the measured value of a variable parameter exceeds either of these two thresholds, attention is required; when the measured value of a variable parameter exceeds both thresholds simultaneously, intervention is necessary.
[0041] Specifically, taking the containment atmosphere monitoring system (CAM) in a certain type of intelligent operation monitoring system as an example, the step-by-step thresholds, dynamic thresholds, and empirical thresholds of its variable parameters are as follows: Figure 3 As shown, the variable parameters of the CAM system are affected by multiple factors such as the unit's operating status and the external environment. Figure 3 The data in this document represents short-term data for the system within a certain period and does not represent the typical state of the system throughout the entire lifespan of the unit.
[0042] like Figure 4 The diagram shows a detailed flowchart of a design method for variable parameter classification and early warning in an intelligent operation monitoring system for nuclear power plants, according to an embodiment of the present invention.
[0043] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0044] Example 2
[0045] According to another aspect of the embodiments of this application, the present invention also provides a design device for classifying and issuing early warnings of variable parameters in a nuclear power plant intelligent operation monitoring system. For example... Figure 5 As shown, the device includes: The acquisition module 501 is used to acquire all variable parameters and their measured values in the intelligent operation monitoring system of the nuclear power plant. The grading module 502 is used to grade all variable parameters based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet either are non-core variable parameters. The core variable parameter early warning module 503 is used to generate thresholds for core variable parameters using the comparison method, residual method and empirical method respectively, and to issue early warnings based on the comparison results between the measured values of the core variable parameters and the thresholds. The non-core variable parameter early warning module 504 is used to generate thresholds for non-core variable parameters using both comparative and empirical methods, and to issue early warnings based on the comparison results between the measured values of the non-core variable parameters and the thresholds.
[0046] As an optional approach, for core variable parameters, thresholds are generated using the comparison method, residual method, and empirical method, respectively. Specifically, for core variable parameters, a first distribution threshold, a residual threshold, and a first empirical threshold are generated using the comparison method, residual method, and empirical method, respectively.
[0047] As an optional approach, and based on the comparison between the measured values of the core variable parameters and the thresholds, early warning is issued, including using the first distribution threshold, the residual threshold, and the first empirical threshold as judgment values; when the measured value of the core variable parameter exceeds any one of the judgment values, an alert signal of concern is issued; when the measured value of the core variable parameter exceeds any two of the judgment values, an intervention alert signal is issued.
[0048] As an alternative approach, for non-core variable parameters, thresholds are generated using both comparative and empirical methods, including generating a second distribution threshold and a second empirical threshold using both comparative and empirical methods.
[0049] As an optional approach, and based on the comparison between the measured values of non-core variable parameters and thresholds, early warning is issued, including using the second distribution threshold and the second empirical threshold as judgment values; when the measured value of a non-core variable parameter exceeds any one of the judgment values, an alert signal is issued; when the measured value of a non-core variable parameter exceeds two of the judgment values simultaneously, an intervention alert signal is issued.
[0050] As an optional approach, safety evaluation indicators include logical operation variables for stopping reactor operation, variables for characterizing operating limitations and conditions under various operating conditions of the unit, variables for characterizing monitoring requirements under various operating conditions of the unit, variables for periodic inspections of the primary and secondary circuit main control rooms, and variables for safety risk probabilities exceeding preset limits.
[0051] As an optional approach, the economic evaluation indicators include logical operation variables used to stop the turbine operation, variables that would cause the unit to immediately stop generating electricity, variables that would cause the unit to stop generating electricity for more than 3 days, variables that would cause the unit to reduce its power output to less than or equal to 90% of its rated power, and variables whose economic risk probability is higher than a preset limit.
[0052] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.
[0053] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0054] Example 3
[0055] According to one aspect of this application, a computer program product is provided, the computer program product comprising a computer program.
[0056] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0057] Figure 6 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present application is shown.
[0058] It should be noted that, Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0059] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM). The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output interface 605 (I / O interface) is also connected to the bus 604.
[0060] The following components are connected to the input / output interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a local area network card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.
[0061] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit 601, it performs various functions defined in the system of this application.
[0062] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit 601, it performs various functions provided in the embodiments of this application.
[0063] Example 4
[0064] According to another aspect of the embodiments of this application, an electronic device is also provided for a design method of variable parameter classification and early warning in a smart operation monitoring system for nuclear power plants. This embodiment uses this electronic device as an example of a terminal device. Figure 7 As shown, the electronic device includes a memory 702 and a processor 704. The memory 702 stores a computer program, and the processor 704 is configured to execute the steps of any of the above method embodiments through the computer program.
[0065] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.
[0066] Optionally, in this embodiment, the processor may be configured to execute the methods in the embodiments of this application via a computer program.
[0067] Alternatively, as those skilled in the art will understand, Figure 7 The structure shown is for illustrative purposes only. Figure 7 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 7 The more or fewer components shown (such as network interfaces, etc.), or having the same Figure 7 The different configurations shown.
[0068] The memory 702 can be used to store software programs and modules, such as the program instructions / modules corresponding to the design method and device for variable parameter classification and early warning of a nuclear power plant intelligent operation monitoring system in this embodiment. The processor 704 executes various functional applications and data processing by running the software programs and modules stored in the memory 702, thereby realizing the aforementioned design method for variable parameter classification and early warning of a nuclear power plant intelligent operation monitoring system. The memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 702 may further include memory remotely located relative to the processor 704, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 702 may be used, but is not limited to, to store all variable parameters and their measured values. As an example, such as... Figure 7 As shown, the memory 702 may include, but is not limited to, the acquisition module 501, the hierarchical module 502, the core variable parameter early warning module 503, and the non-core variable parameter early warning module 504 from the aforementioned device. Furthermore, it may include, but is not limited to, other module units from the aforementioned device, which will not be elaborated upon in this example.
[0069] Optionally, the transmission device 706 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 706 includes a Network Interface Controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 706 is a radio frequency (RF) module, used for wireless communication with the Internet.
[0070] In addition, the aforementioned electronic device also includes: a display 708 for displaying warning information; and a connection bus 710 for connecting various module components in the aforementioned electronic device.
[0071] In other embodiments, the aforementioned terminal device or server can be a node in a distributed system, wherein the distributed system can be a blockchain system, which is a distributed system formed by connecting multiple nodes through network communication. The nodes can form a peer-to-peer network, and any form of computing device, such as a server, terminal, or other electronic device, can become a node in the blockchain system by joining this peer-to-peer network.
[0072] Example 5
[0073] According to one aspect of this application, a computer-readable storage medium is provided, wherein a processor of an electronic device reads computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the electronic device to perform the design method for classifying and warning variable parameters of a nuclear power plant intelligent operation monitoring system provided in various optional implementations of the above-described design method for classifying and warning variable parameters of a nuclear power plant intelligent operation monitoring system.
[0074] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store methods for performing the embodiments of this application.
[0075] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0076] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0077] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of this application.
[0078] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0079] In the several embodiments provided in this application, it should be understood that the disclosed application can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.
[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0081] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
[0083] By applying the above-described technical solution of the present invention, at least the following technical effects are achieved: 1. This invention combines variable parameter classification with customized early warning strategies to achieve efficient and accurate management of tens of thousands of variable parameters in the intelligent operation monitoring system of nuclear power plants, which greatly improves the safety and economy of nuclear power operation. Through automated identification and early warning, it reduces the burden of manual monitoring and the risk of misjudgment, enhances the system's sensitivity to early anomalies and response speed, and provides strong support for the intelligent operation of nuclear power units. 2. By introducing identification methods such as comparison method, residual method and empirical method, this invention can automatically monitor and classify variable parameters, reduce the manual monitoring tasks of the main control room operator, improve the accuracy and timeliness of anomaly detection, and reduce the risk of human error. 3. By establishing a set of core variable parameters and a set of non-core variable parameters, and by formulating differentiated early warning strategies, this invention can more effectively identify early abnormal signals and notify operators in a timely manner, even in complex and dynamic nuclear power operating environments, thus preventing the escalation of potential accidents. 4. By classifying variable parameters according to their importance to nuclear power safety and economy, this invention can more rationally allocate limited monitoring resources and technical support, ensuring real-time monitoring and rapid response of key parameters.
[0084] The above are merely several specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.
[0086] It should be noted that, in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
Claims
1. A design method for classifying and issuing early warnings of variable parameters in an intelligent operation monitoring system for nuclear power plants, characterized in that, include: Obtain all variable parameters and their measured values from the intelligent operation monitoring system of a nuclear power plant; All variable parameters are classified into levels based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of the safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet any of them are non-core variable parameters. For core variable parameters, thresholds are generated using the comparison method, residual method, and empirical method, respectively, and early warnings are issued based on the comparison results between the measured values of the core variable parameters and the thresholds. For non-core variable parameters, thresholds are generated using both comparative and empirical methods, and warnings are issued based on the comparison between the measured values of the non-core variable parameters and the thresholds.
2. The design method according to claim 1, characterized in that, The thresholds for the core variable parameters are generated using comparison, residual, and empirical methods, respectively, including: For the core variable parameters, the first distribution threshold, residual threshold, and first empirical threshold are generated using the comparison method, residual method, and empirical method, respectively.
3. The design method according to claim 2, characterized in that, The early warning system, which compares the measured values of core variable parameters with the threshold, includes: The first distribution threshold, the residual threshold, and the first empirical threshold are used as the judgment values; When the measured value of the core variable parameter exceeds any one of the judgment values, a warning signal for attention is issued; when the measured value of the core variable parameter exceeds any two of the judgment values, a warning signal for intervention is issued.
4. The design method according to claim 1, characterized in that, For non-core variable parameters, the thresholds generated using both comparative and empirical methods include: For non-core variable parameters, the second distribution threshold and the second empirical threshold are generated using the comparison method and the empirical method, respectively.
5. The design method according to claim 4, characterized in that, The early warning system, which is based on the comparison between the measured values of non-core variable parameters and the threshold, includes: The second distribution threshold and the second empirical threshold are used as the judgment values; When the measured value of a non-core variable parameter exceeds any one of the judgment values, a warning signal for attention is issued; when the measured value of a non-core variable parameter exceeds two of the judgment values simultaneously, a warning signal for intervention is issued.
6. The design method according to claim 1, characterized in that, The safety evaluation indicators include logical operation variables for stopping reactor operation, variables for characterizing operating limitations and conditions under various operating conditions of the unit, variables for characterizing monitoring requirements under various operating conditions of the unit, variables for periodic inspections of the primary and secondary circuit main control rooms, and variables for safety risk probabilities exceeding preset limits.
7. The design method according to claim 1, characterized in that, The economic evaluation indicators include logical operation variables used to stop the operation of the steam turbine, variables that would cause the unit to stop generating electricity immediately due to an anomaly, variables that would cause the unit to stop generating electricity for more than or equal to 3 days due to an anomaly, variables that would cause the unit to reduce its power output to less than or equal to 90% of its rated power due to an anomaly, and variables whose economic risk probability is higher than a preset limit.
8. A design device for classifying and issuing early warnings of variable parameters in an intelligent operation monitoring system for nuclear power plants, characterized in that, include: The acquisition module is used to acquire all variable parameters and their measured values in the intelligent operation monitoring system of the nuclear power plant; The grading module is used to classify all variable parameters based on safety evaluation indicators and economic evaluation indicators. Among them, those that meet any one of the sub-indicators of the safety evaluation indicators or economic evaluation indicators are core variable parameters, and those that do not meet either are non-core variable parameters. The core variable parameter early warning module is used to generate thresholds for core variable parameters using comparison method, residual method and empirical method respectively, and to issue early warnings based on the comparison results between the measured value of the core variable parameter and the threshold. The non-core variable parameter early warning module is used to generate thresholds for non-core variable parameters using both comparative and empirical methods, and to issue early warnings based on the comparison results between the measured values of the non-core variable parameters and the thresholds.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed by an electronic device to perform the method described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 7.
11. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 7 through the computer program.