Method, device and storage medium for online monitoring of risks of nuclear power plant

Abstract

The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of nuclear power plant risk online monitoring method, equipment and storage medium.The present application relates to the experimental technical field, and propose a kind of

Description

Technical Field

[0001] This invention relates to the field of nuclear power plant reactor technology, and in particular to a method, equipment and storage medium for online risk monitoring of nuclear power plants. Background Technology

[0002] As a clean and efficient energy source, nuclear power's operational safety has always been the lifeline of its development. Probabilistic safety analysis, a key technology for overall safety assessment of nuclear power plants, has been widely applied in the nuclear power industry. With the continuous improvement of nuclear safety regulatory standards and the deepening of risk-guided applications, nuclear power plant risk monitors have gradually become an indispensable core system in supporting the daily operation of units.

[0003] To support the online calculations of massive models, nuclear power plants typically equip their risk monitoring systems with high-performance computing servers. However, in actual operation, nuclear power units operate at full power in a steady state for most of the time during fuel cycles that can last for more than a dozen months. During this steady-state operation, the plant's equipment configuration remains unchanged for extended periods, leaving these high-end servers in a near-idle state, unable to utilize their normally idle computing power to alleviate computational pressure during critical moments. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to propose a method, equipment, and storage medium for online risk monitoring of nuclear power plants, so as to improve the contradiction between slow speed and large error in large-scale online model calculations, and to improve the utilization efficiency of idle computing power during the steady-state operation of nuclear power plants.

[0005] To achieve the above and other related objectives, this invention proposes an online risk monitoring method for nuclear power plants, applied to the operation system of nuclear power plants, comprising the following steps: Obtain a preset power plant configuration list, wherein the power plant configuration list contains multiple configurations to be calculated; In background silent mode: The calculation results are obtained based on the power plant configuration list. The status feature code is obtained based on the unique identifier of all unavailable devices in the configuration configuration, and the status feature code is associated with the corresponding calculation result and stored in the pre-calculation result database. If you receive device status change information or trial input: Generate a real-time signature based on the unique identifier of all currently unavailable devices; The system searches the pre-calculation result database for a status feature code that matches the real-time feature code. If a status feature code exists, the system retrieves and outputs the corresponding accurate calculation result. If a status feature code does not exist, the system redirects the calculation to the existing calculation engine for on-site calculation.

[0006] In a specific embodiment of the present invention, the step of obtaining a preset power plant configuration list includes identifying and generating the configuration to be calculated through multiple channels, including: It connects to the power plant's production management system and automatically reads preventive maintenance work orders, corrective maintenance work orders, and periodic test plans for the future planned cycle to extract the unavailability status of the relevant equipment. Connect to the historical event database to extract multiple device failure combinations that occur more frequently than a preset threshold. Based on the set of equipment planned to be decommissioned in the future, the remaining healthy equipment is traversed, and the dynamic risk enhancement value of the equipment in a specific defective state is calculated. If the dynamic risk enhancement value reaches a preset importance threshold, the combination of the equipment and the set of equipment is included in the list to be calculated as a high-risk supplementary scenario.

[0007] In a specific embodiment of the present invention, the step of obtaining the preset power plant configuration list further includes: Based on historical operational experience data, extract real single-device failure events, multi-device failure events, or multiple failure combinations that have occurred in history, and incorporate these events as scenarios to be calculated into the power plant configuration list.

[0008] In one specific embodiment of the present invention, the historical operational experience data includes an operational experience database, license operational event reports, and / or historical event records.

[0009] In a specific embodiment of the present invention, the step of obtaining a preset power plant configuration list includes: Search the operating technical specifications database and remove equipment combinations that do not contain any restrictive safety-related equipment covered by operating technical specifications; According to the operational technical specifications, if a certain equipment combination will directly trigger the reactor's automatic protection action or force a manual shutdown within a preset time, then that equipment combination will be removed from the list to be calculated.

[0010] In a specific embodiment of the present invention, the step of obtaining a preset power plant configuration list includes: Provide a human-machine collaboration interface to receive manually entered specific combinations of device failures and merge them with an automatically generated list to remove duplicates, thereby generating a complete list to be calculated.

[0011] In a specific embodiment of the present invention, the step of obtaining a preset power plant configuration list includes: The list of the complete set to be calculated after merging and deduplication is intelligently queued according to a comprehensive weight to determine the order of background calculation. The comprehensive weight is obtained based on the preset time urgency and probability of occurrence.

[0012] In one specific embodiment of the present invention, the time urgency is determined based on the start time of the future maintenance plan, and the probability of occurrence is determined based on historical occurrence frequency and / or operational experience.

[0013] In a specific embodiment of the present invention, the step of obtaining a preset power plant configuration list includes: If the baseline risk model of a nuclear power plant is updated, an interrupt command is automatically sent to the background silent calculation engine to clear the current queue, regenerate the configuration list according to the new model, and overwrite the original old calculation results in the pre-calculation result database.

[0014] In one specific embodiment of the present invention, the generation of the state feature code adopts a hash mapping mechanism: The unique identifiers of all unavailable devices in the device status combination are arranged in lexicographical order, concatenated into a standard string, and then a fixed-length hash value is calculated using a hash algorithm.

[0015] In a specific embodiment of the present invention, the step of obtaining the calculation result based on the power plant configuration list includes: The calculation results are obtained by using an accurate solution engine, which is an accurate solution engine based on a binary decision graph or a zero-compressed binary decision graph. It is used to perform full model traversal and accurate solution of the scenarios in the power plant configuration list to eliminate truncation error.

[0016] In a specific embodiment of the present invention, the original computing engine adopts the cut set method. If no match is found in the pre-calculation result database, the failure probability of the basic event is dynamically modified and algebraic operations are performed using the pre-solved list of minimum cut sets of the basic model to quickly output approximate calculation results on site.

[0017] In one specific embodiment of the present invention, the precise calculation results include core damage frequency, early mass radioactive release frequency, precise minimum cut set, and configuration management recommendations.

[0018] In a specific embodiment of the present invention, the step of obtaining the status feature code based on the unique identifiers of all unavailable devices in the configuration configuration includes: The unique identifier is the device ID, which includes: extracting the device IDs of all unavailable devices in the device status combination, and sorting them in ascending order according to a mixed alphabetical order of letters and numbers to obtain a sorted sequence of device IDs; The device IDs in the device ID sequence are concatenated sequentially using a preset delimiter to generate a standard string.

[0019] The present invention also proposes an online risk monitoring device for nuclear power plants, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the online risk monitoring method for nuclear power plants.

[0020] The present invention also proposes a storage medium storing a computer program, characterized in that the program, when executed by a processor, implements the aforementioned online risk monitoring method for nuclear power plants.

[0021] The technical advantages of this invention are as follows: This invention balances computational speed and accuracy. Because this application moves the computation process of high-precision binary decision graphs or zero-compressed binary decision graph algorithms to an offline background, it utilizes the idle computing power during the daily steady-state operation of nuclear power plants to perform full model traversal and accurate solution in advance, and stores the calculation results in a pre-calculation result database in the form of state feature codes. On the online end, the system prioritizes comparing the real-time feature codes with the database; if a match is found, it directly calls the accurate calculation results, thus achieving "millisecond-level" direct calling. This essentially eliminates the risks of space explosion and lag caused by online accurate calculations, while avoiding the risk of missed detection caused by truncation errors in cut-set approximation algorithms, greatly improving the accuracy and response speed of online risk monitoring in nuclear power plants. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the online risk monitoring method for nuclear power plants according to one embodiment of the present invention. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and its component layout may also be more complex.

[0026] As a clean and efficient energy source, nuclear power's operational safety has always been the lifeline of its development. Probabilistic safety analysis, a key technology for overall safety assessment of nuclear power plants, has been widely applied in the nuclear power industry. With the continuous improvement of nuclear safety regulatory standards and the deepening of risk-guided applications, nuclear power plant risk monitors have gradually become an indispensable core system in the daily operation support of units. Their core function is to dynamically assess quantitative safety indicators such as the current core damage frequency and the frequency of early large-scale radioactive releases when real-time changes occur in the plant's operating configuration, such as equipment maintenance, periodic testing, or sudden degrades, providing scientific support for operational decisions.

[0027] A nuclear power plant risk monitor is a computer application system that performs quantitative risk assessment based on the real-time operating status of the power plant. It correlates the real-time equipment status of the power plant with the underlying probabilistic safety analysis model to calculate the system failure probability and overall plant risk indicators in real time. In recent years, third-generation advanced pressurized water reactors have been widely constructed domestically and globally. The safety systems of these reactors typically employ highly complex redundancy designs, most notably the completely physically separated three-stage dedicated safety systems. This design directly leads to a dramatic increase in the size of the underlying fault tree model, generally consisting of tens of thousands of interwoven logic gates and basic event nodes, significantly increasing model complexity. Furthermore, according to regulatory requirements, the scope of the nuclear power plant risk monitor model has gradually expanded from traditional internal event-level probabilistic safety analysis to full-range probabilistic safety analysis, further exacerbating the computational burden.

[0028] Currently, online solution engines for risk monitors primarily employ two technical approaches, both of which suffer from insurmountable shortcomings. The first approach uses approximation algorithms. Taking the most widely used cutset method as an example, this method pre-calculates a minimal cutset list under the basic model using either an uplink or downlink method. During online monitoring, the system extracts real-time collected equipment status and operating parameters, dynamically modifies the failure probability of basic events, and substitutes them into this pre-set list for algebraic calculations, thereby quickly deriving quantitative indicators such as core damage frequency. The drawback of this approach is that the minimal cutset list it relies on is an "incomplete list" pre-generated based on the specific basic state of the power plant and pre-set probability cutoff values. When the actual configuration of the power plant changes, cutsets that were previously "truncated" due to low probability are highly likely to jump to high-risk combinations under the current configuration. Because these have been pre-removed from the pre-set list, the system will completely miss this surge in risk, leading to distorted quantitative results and potentially providing misleading recommendations for risk management configuration.

[0029] The second technical approach employs precise algorithms, such as binary decision graphs or zero-compressed binary decision graphs. To eliminate the truncation error caused by the aforementioned cut-set method and ensure computational accuracy, this approach requires the underlying computational engine to re-traverse and solve the entire risk model every time the power plant's operating status changes. Its core drawbacks are state space explosion and response delay. The risk model relied upon by the nuclear power plant risk monitor is an ultra-large Boolean logic structure composed of tens of thousands of logic gates. Even with complex logic simplification and algorithm optimization in the early stages, the computational overhead of full-range re-solution remains extremely large, with a single quantization calculation potentially taking several minutes or even longer. Moreover, when faced with complex concurrent equipment maintenance or transient degradation, the underlying algorithm is highly susceptible to state space exponential explosion, leading to severe memory overflows and prolonged system lag, failing to meet the operators' stringent requirements for rapid decision support in emergency and abnormal situations.

[0030] Furthermore, to support the online calculations of massive models, nuclear power plants typically equip their risk monitors with high-performance computing servers. However, in actual operation, nuclear power units operate at full power in a steady state for most of the fuel cycle, which can last for more than a dozen months. During this steady-state operation, the plant's equipment configuration remains unchanged for extended periods, leaving these high-end servers almost idle. This prevents the utilization of normally idle computing power to alleviate computational pressure during critical moments. Additionally, while risk monitors do possess offline calculation or "hypothesis analysis" capabilities, they are mostly used merely as "trial calculation tools" for operators. The technology does not store these pre-calculated high-risk combination results in a structured database, meaning the results obtained from the initial computational effort cannot be directly accessed during online applications.

[0031] First, the technical terms and custom concepts involved in this application specification will be defined and explained in a concentrated manner.

[0032] A status signature is a fixed-length hash value used to uniquely identify a specific combination of unavailable equipment in a nuclear power plant. In this embodiment, the status signature is calculated by concatenating the unique identifiers of all unavailable equipment in the configuration configuration in ascending order according to a mixed alphabetical and numeric dictionary order into a standard string, and then using a specific hash algorithm. The status signature serves as a primary key in the pre-calculation result database and is associated with the corresponding precise calculation result.

[0033] Real-time signature code: This refers to a hash value generated in real time during the actual operation of a power plant when equipment status changes or when manual calculation input is received at the human-machine interface. It is used for retrieval and matching in a pre-calculated result database. The generation algorithm, equipment identifier extraction rules, sorting rules, concatenation rules, and hash algorithm for the real-time signature code are completely consistent with the generation rules for the status signature code, ensuring that the real-time signature code generated under the same combination of unavailable equipment states is completely identical to the status signature code.

[0034] The accurate solution engine is a computational program built on the Binary Decision Diagram (BDD) or Zero-suppressed Binary Decision Diagram (ZBDD) algorithm. It performs a full model traversal and accurate solution for the ultra-large Boolean logic structure of nuclear power plants. The accurate solution engine does not introduce probability cutoff values ​​during the calculation process, eliminating truncation errors caused by actively discarding low-probability cut sets, thus outputting absolutely accurate risk quantification indicators.

[0035] The original computing engine refers to a backup computing program built on traditional cutset approximation algorithms, used for rapid on-site calculations when the pre-calculated result database fails to match. The original computing engine utilizes a pre-solved and stored list of minimum cutsets for the basic model, dynamically modifies the failure probabilities of basic events, and performs algebraic operations to output approximate risk quantification indicators on-site in a very short time, serving as a safety fallback mechanism for the system.

[0036] Dynamic risk enhancement value: This refers to the factor by which the system risk level increases after a healthy device fails under a specific system incomplete state. Dynamic risk enhancement value differs from static risk enhancement value under a static baseline model. It is based on the premise of a set of currently or planned decommissioned devices and can quantitatively reflect the dynamic amplification effect of healthy devices on system risk under specific incomplete operating conditions.

[0037] Operating technical specifications refer to the restrictive technical requirements that nuclear power plants must comply with, as approved by nuclear safety regulatory authorities. These specifications define the operational limitations, safety limits, and monitoring requirements for nuclear power plants under various operating modes. In this embodiment, the operating technical specifications are used as the rule source for computational power interception and filtering.

[0038] Time urgency score: This is a numerical value used to quantitatively assess how urgency a configuration configuration is before its planned execution time. The time urgency score is inversely proportional to the difference between the planned execution time and the current system time; the closer to the planned execution time, the higher the score, thus assigning a higher calculation priority to the configuration configuration.

[0039] Probability score: This is a numerical value used to quantitatively assess the probability or frequency of a given configuration occurring during the historical operation of a nuclear power plant. The probability score is calculated by normalizing the cumulative occurrence frequency of the configuration or similar multiple failure combinations in the historical event database. The higher the historical occurrence frequency, the higher the score.

[0040] To solve the above technical problems, such as Figure 1 As shown, the online risk monitoring method for nuclear power plants provided by this invention includes the following steps: To achieve the above and other related objectives, this invention proposes an online risk monitoring method for nuclear power plants, applied to the operation system of nuclear power plants, comprising the following steps: Step S1: Generate a list of preset power plant configuration configurations. The configuration configurations to be calculated are identified and generated through multiple channels, and computing power is intercepted and filtered based on operational technical specifications. Combined with human-machine collaborative supplementation and intelligent queue sorting, a final complete list of configurations to be calculated is generated.

[0041] Step S2: Background Silent Precise Solving and Result Database Construction. Utilizing the idle computing power during the daily steady-state operation of the nuclear power plant, the precise solver engine is invoked in a background silent state to perform precise solving on the configuration configuration in the manifest, obtaining the corresponding precise calculation results. The unique identifiers of all unavailable devices in the configuration configuration are arranged in lexicographical order and concatenated. A unique status feature code is calculated using a hash algorithm, and the status feature code is associated with the corresponding precise calculation result and stored in the pre-calculation result database.

[0042] Step S3: Online comparison and diversion mechanism under abnormal transient conditions. When the power plant experiences an actual equipment status change or receives trial calculation input, the unique identifiers of all currently unavailable equipment are extracted and arranged in the same lexicographical order, concatenated, and hashed to generate real-time feature codes. The pre-calculation result database is searched to see if there is a status feature code that matches the real-time feature code. If it exists, the corresponding accurate calculation result is directly retrieved and output. If it does not exist, the calculation is automatically diverted to the original calculation engine for on-site calculation.

[0043] Step S1 specifically includes the following sub-steps: Step S11: Multi-channel identification and generation of configuration configurations to be computed. To ensure that the pre-computed scenarios can cover future high-risk operating conditions and avoid the waste of computing power caused by low-value computation, the system identifies and generates scenarios through three parallel channels.

[0044] Step S111: Connect to the production management system to obtain plans. The inventory generation and filtering module 180 automatically connects to the power plant production management system 110, reads preventive maintenance work orders, corrective maintenance work orders, and periodic test plans for a future planning cycle (e.g., within the next 30 days), and automatically extracts the unavailability status of the relevant equipment. This allows the system to know in advance the equipment downtime that will be scheduled for the future.

[0045] Step S112: Extract failure combinations from the historical event database. The inventory generation and filtering module directly connects to the historical event database to extract multiple equipment failure combinations that have occurred more frequently than a preset threshold in the license operation event reports and operational experience. For example, combinations of multiple equipment failure events that have actually occurred in the past or have a high frequency of occurrence (such as a busbar power failure combined with a water pump maintenance) are directly used as input scenarios that must be pre-calculated. This avoids relying solely on planned work orders and ignoring historically high-frequency sudden failures.

[0046] Step S113: RAW mutation supplementation based on specific states. Using a set of equipment planned for future decommissioning (e.g., equipment in a planned maintenance safety dedicated system) as a premise, the system background calls a fast calculation engine to iterate through the remaining healthy equipment and calculate its dynamic risk enhancement value under a specific defective state. If the dynamic risk enhancement value is greater than or equal to a preset importance threshold, it indicates that the equipment has a significant amplification effect on system risk. The system automatically adds the equipment and its combination with the equipment set as high-risk supplementary scenarios to the calculation list.

[0047] Step S12: Scene filtering based on runtime technical specifications (TS). To prevent scene space explosion caused by an excessive number of device combinations requiring computation, the system introduces a scene filtering mechanism based on runtime technical specifications as an intelligent computing power interception method.

[0048] Step S121: Exclude equipment outside the TS (Technical Specification) scope. The inventory generation and filtering module 180 searches the operating technical specification database and compares the generated equipment combinations. If a combination of equipment does not contain any restrictive safety-related equipment covered by the operating technical specifications, it is removed. Because the configuration risk management application of the nuclear power plant risk online monitoring system primarily aims to support the management of the operating technical specifications (TS), equipment combinations outside the TS scope contribute very little to the instantaneous safety risks of the power plant and do not require valuable pre-computational resources.

[0049] Step S122: Exclude equipment combinations that could cause immediate reactor shutdown. The inventory generation and screening module compares the generated equipment with the operating technical specifications. If a generated equipment or equipment combination violates the TS regulations, causing the unit to immediately trigger automatic reactor protection actions (automatic shutdown) or forcibly require manual shutdown within a preset time, then the equipment combination is directly removed from the inventory to be calculated.

[0050] Step S130: Human-machine collaboration and dynamic maintenance. The system introduces a human-machine collaboration and dynamic maintenance mechanism to integrate human experience and rationally schedule pre-computational resources.

[0051] Step S131: Manual Input Supplementation and Deduplication Merging. The system provides a manual supplementation interface through a human-computer interaction interface, allowing operators or engineers to manually input specific equipment failure combinations (such as switching between normal and standby columns, specific test conditions, etc.). The manually entered scenarios are merged and deduplicated with the list automatically generated by the system to form the final "List of Complete Sets to be Calculated".

[0052] Step S132: Intelligent Queue Sorting. The system performs intelligent queue sorting on the merged and deduplicated list of the complete set to be calculated. The system assigns weights based on "priority of time" supplemented by "priority of occurrence," and calculates the comprehensive weight score for each scenario.

[0053] Step S133: Baseline Model Update Triggers Interruption and Reconfiguration. The baseline risk model for nuclear power plants is typically updated annually to reflect the latest design changes, equipment replacements, and operational experience. When the baseline risk model is updated, the system automatically sends an interrupt command to the background silent calculation engine. Upon receiving the interrupt command, the background silent calculation engine immediately stops the currently executing calculation task and clears the current queue of pending calculations. Subsequently, the system re-executes step S1, generating the preset power plant configuration list, according to the new model, and regenerates the configuration list. The background silent calculation engine performs a precise solution again based on the newly generated list and overwrites the original old calculation results in the pre-calculation result database, ensuring that the pre-calculation data remains absolutely consistent with the latest baseline risk model.

[0054] For step S200 (background silent precise calculation and result database construction), its core lies in utilizing the idle computing power during the daily steady-state operation of the nuclear power plant, calling the precise solution engine in the background silent state to perform precise solution of the configuration configuration in the list, and using a hash mapping mechanism to construct a pre-calculated result database.

[0055] In this embodiment, the precise solution engine is a precise solution engine based on Binary Decision Graph (BDD) or Zero-Compressed Binary Decision Graph (ZBDD). In a silent background state, the silent background computing engine 130 utilizes the idle computing power during the nuclear power plant's daily steady-state operation to perform a full model traversal of each configuration configuration in the power plant's configuration list. The BDD / ZBDD algorithm transforms the ultra-large Boolean logic structure (fault tree model) into a directed acyclic graph, and without probabilistic truncation, re-traverses and precisely solves the entire risk model, thereby calculating the absolutely accurate core damage frequency, the early large-scale radioactive release frequency, and the precise minimum cut set.

[0056] Traditional online risk monitors typically employ cut-set approximation algorithms to meet the demand for ultra-fast responses within seconds. Cut-set methods generate an "incomplete list" based on specific power plant conditions and preset probability cutoff values. When the actual power plant configuration changes, cut sets that were previously discarded due to extremely low probability may suddenly become high-risk combinations under the current configuration. Because these cut sets have been removed from the preset list in advance, the system completely misses this surge in risk, leading to distorted quantitative results and potentially misleading recommendations for risk management. This embodiment eliminates the risk omissions caused by truncation errors in cut-set approximation algorithms by moving the high-precision BDD / ZBDD algorithm calculation process to an offline background, utilizing idle computing power in a steady state for full model traversal and accurate solution. This ensures the absolute accuracy of the pre-calculated results and provides the most reliable quantitative data support for the safe operation of nuclear power plants.

[0057] After the calculation is completed, the system uses a hash mapping mechanism to generate a unique "status feature code" for each device state combination, so as to achieve efficient storage and fast retrieval of the pre-calculated results. The generation of the status feature code adopts a hash mapping mechanism: the unique identifiers of all unavailable devices in the device state combination are arranged in lexicographical order, concatenated into a standard string, and then a fixed-length hash value is calculated by a hash algorithm, which serves as the unique status feature code for that combination.

[0058] To ensure the uniqueness and standardization of the generated signature, the process of arranging and concatenating device IDs is explained in detail. The unique identifier is the device ID. The system first extracts the device IDs of all unavailable devices from the device state combinations. Then, these device IDs are sorted in ascending order according to a mixed alphanumeric dictionary order, resulting in a sorted sequence of device IDs.

[0059] Finally, regarding step S3 (online comparison and diversion mechanism under abnormal transient conditions), its core lies in realizing dual-channel diversion control through the online comparison and diversion module.

[0060] Step S31: Obtain real-time equipment status changes or manual calculation input. The online comparison and distribution module 150 monitors status changes in the power plant production management system in real time, or receives calculated equipment status input by the operator on the human-machine interface.

[0061] Step S311: Extract the list of unavailable device IDs and sort them lexicographically. The online comparison and distribution module extracts all device IDs that are currently unavailable, sorts them in ascending order according to a mixed alphabetical and numeric dictionary, and concatenates them into a standard string.

[0062] Step S312: Perform hash calculation to generate a real-time signature. The online comparison and distribution module calls the hash calculation module and uses the same hash algorithm as in step S2 (such as MD5 or SHA-256) to calculate the standard string and generate a real-time signature.

[0063] Step S32: Search the pre-calculation result database for matching judgment. The online comparison and diversion module 150 uses the real-time feature code as the key to search the pre-calculation result database to determine whether there is a status feature code that matches the real-time feature code.

[0064] Step S33: Directly retrieve the precise calculation result from the database. If the retrieval is successful, the cache match is considered successful. The online comparison and splitting module directly retrieves the pre-calculated precise calculation result from the pre-calculated result database and jumps to step S35 for display. This greatly shortens the response time on the online end, achieving millisecond-level ultra-fast response.

[0065] Step S34: Automatically redirect the computation to the existing computing engine for on-site calculation. If the retrieval fails, it is determined that the cache is not matched. The system automatically redirects the computation to the existing computing engine for on-site calculation. The existing computing engine uses a cut-set approximation algorithm, which uses a pre-determined list of minimum cut sets of the basic model obtained through uplink or downlink methods to extract the real-time collected equipment status and operating parameters, dynamically modify the failure probability of basic events, and substitute them into the pre-defined list for algebraic calculations, quickly outputting approximate quantitative indicators such as the core damage frequency and the early large-scale radioactive release frequency on-site.

[0066] Step S35: Display the results on the human-computer interaction interface. The obtained calculation results (whether directly retrieved precise results or approximate results calculated on-site) are displayed intuitively on the human-computer interaction interface to provide decision support for operators.

[0067] In summary, the online risk monitoring method and system for nuclear power plants based on dynamic and static hybrid computing provided in this application, through ingenious dynamic and static flow separation architecture design, multi-channel inventory generation, dynamic RAW mutation supplementation, and TS-based computing power interception mechanism, has achieved a comprehensive breakthrough in computing speed, computing accuracy, and computing power resource utilization efficiency for online risk monitoring of nuclear power plants, and has outstanding substantive features and significant progress.

[0068] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

[0069] Throughout this description, numerous specific details, such as examples of components and / or methods, are provided to provide a complete understanding of embodiments of the invention. However, those skilled in the art will recognize that embodiments of the invention may be practiced without one or more of these specific details or by other devices, systems, components, methods, parts, materials, components, etc. In other instances, well-known structures, materials, or operations have not been specifically shown or described in detail to avoid obscuring aspects of embodiments of the invention.

Claims

1. A method for online risk monitoring in nuclear power plants, characterized in that, Applied to nuclear power plant operating systems, it includes the following steps: Obtain a preset power plant configuration list, wherein the power plant configuration list contains multiple configurations to be calculated; In background silent mode: The calculation results are obtained based on the power plant configuration list. The status feature code is obtained based on the unique identifier of all unavailable devices in the configuration configuration, and the status feature code is associated with the corresponding calculation result and stored in the pre-calculation result database. If you receive device status change information or trial input: Generate a real-time signature based on the unique identifier of all currently unavailable devices; The system searches the pre-calculation result database for a status feature code that matches the real-time feature code. If a status feature code exists, the system retrieves and outputs the corresponding accurate calculation result. If a status feature code does not exist, the system redirects the calculation to the existing calculation engine for on-site calculation.

2. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, In the step of obtaining the preset power plant configuration list, the configuration configuration to be calculated is identified and generated through multiple channels, including: It connects to the power plant's production management system and automatically reads preventive maintenance work orders, corrective maintenance work orders, and periodic test plans for the future planned cycle to extract the unavailability status of the relevant equipment. Connect to the historical event database to extract multiple device failure combinations that occur more frequently than a preset threshold. Based on the set of equipment planned to be decommissioned in the future, the remaining healthy equipment is traversed, and the dynamic risk enhancement value of the equipment in a specific defective state is calculated. If the dynamic risk enhancement value reaches a preset importance threshold, the combination of the equipment and the set of equipment is included in the list to be calculated as a high-risk supplementary scenario.

3. The method for online risk monitoring of nuclear power plants according to claim 2, characterized in that, The step of obtaining the preset power plant configuration list also includes: Based on historical operational experience data, extract real single-device failure events, multi-device failure events, or multiple failure combinations that have occurred in history, and incorporate these events as scenarios to be calculated into the power plant configuration list.

4. The method for online risk monitoring of nuclear power plants according to claim 3, characterized in that, The historical operational experience data includes an operational experience database, licensed operational event reports, and / or historical event records.

5. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the preset power plant configuration list includes: Search the operating technical specifications database and remove equipment combinations that do not contain any restrictive safety-related equipment covered by operating technical specifications; According to the operational technical specifications, if a certain equipment combination will directly trigger the reactor's automatic protection action or force a manual shutdown within a preset time, then that equipment combination will be removed from the list to be calculated.

6. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the preset power plant configuration list includes: Provide a human-machine collaboration interface to receive manually entered specific combinations of device failures and merge them with an automatically generated list to remove duplicates, thereby generating a complete list to be calculated.

7. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the preset power plant configuration list includes: The list of the complete set to be calculated after merging and deduplication is intelligently queued according to a comprehensive weight to determine the order of background calculation. The comprehensive weight is obtained based on the preset time urgency and probability of occurrence.

8. The method for online risk monitoring of nuclear power plants according to claim 7, characterized in that, The time urgency is determined based on the start time of the future maintenance plan, and the probability of occurrence is determined based on historical frequency and / or operational experience.

9. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the preset power plant configuration list includes: If the baseline risk model of a nuclear power plant is updated, an interrupt command is automatically sent to the background silent calculation engine to clear the current queue, regenerate the configuration list according to the new model, and overwrite the original old calculation results in the pre-calculation result database.

10. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The state feature code is generated using a hash mapping mechanism: The unique identifiers of all unavailable devices in the device status combination are arranged in lexicographical order, concatenated into a standard string, and then a fixed-length hash value is calculated using a hash algorithm.

11. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the calculation result based on the power plant configuration list includes: The calculation results are obtained by using an accurate solution engine, which is an accurate solution engine based on a binary decision graph or a zero-compressed binary decision graph. It is used to perform full model traversal and accurate solution of the scenarios in the power plant configuration list to eliminate truncation error.

12. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The original computing engine uses the cut set method. If no match is found in the pre-calculated result database, the failure probability of the basic event is dynamically modified and algebraic operations are performed using the pre-solved list of minimum cut sets of the basic model to quickly output approximate calculation results on site.

13. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The precise calculation results include core damage frequency, early mass radioactive release frequency, precise minimum cut set, and configuration management recommendations.

14. The method for online risk monitoring of nuclear power plants according to claim 1, characterized in that, The step of obtaining the status feature code based on the unique identifiers of all unavailable devices in the configuration configuration includes: The unique identifier is the device ID, which includes: extracting the device IDs of all unavailable devices in the device status combination, and sorting them in ascending order according to a mixed alphabetical order of letters and numbers to obtain a sorted sequence of device IDs; The device IDs in the device ID sequence are concatenated sequentially using a preset delimiter to generate a standard string.

15. An online risk monitoring device for nuclear power plants, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the online risk monitoring method for nuclear power plants as described in any one of claims 1 to 14.

16. A storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the online risk monitoring method for nuclear power plants as described in any one of claims 1 to 14.

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