Anomaly handling methods, systems, and related media based on nuclear power plant technical specifications

Abstract

This application provides an anomaly handling method, system, and related media based on nuclear power plant technical specifications, belonging to the field of nuclear power plant operation and management technology. The method includes: acquiring real-time operating data of the nuclear power plant; wherein the real-time operating data is configured with system identification information identifying the data source; determining the target operating mode of the nuclear power plant based on the real-time operating data; matching an applicable target rule group from a set of preset event rule groups according to the system identification information and the target operating mode; wherein the event rule group is constructed based on the content of the technical specifications; and identifying anomalies in the real-time operating data based on the target rule group, and obtaining target processing information corresponding to the triggered target event rule according to the triggered target event rule. This application enables efficient utilization of technical specifications.

Description

Technical Field

[0001] This application relates to the field of nuclear power plant operation and management technology, and in particular to an anomaly handling method, system and related media based on nuclear power plant technical specifications. Background Technology

[0002] Technical specifications are crucial operational documents for nuclear power plants, but they are voluminous, recording hundreds of events and corresponding measures, making them an essential reference for plant operation. These specifications detail numerous Limiting Conditions for Operation (LCOs), a set of technical parameters that must be met by equipment or systems under different operating modes. These LCOs are designed to ensure the safe operation of the nuclear power plant, defining the acceptable operating range of the system under specific conditions.

[0003] However, current technologies do not efficiently utilize technical specifications. In some cases, relying solely on the operator's memory and paper document searches is inefficient. Even with digitization, operators still need to consult these specifications multiple times. Furthermore, a single piece of equipment or system in a nuclear power plant is often linked to multiple operational constraints, and the complex conditions of a nuclear power plant during operation often trigger multiple events simultaneously. This makes it difficult for operators to understand the situation and implement appropriate countermeasures. Therefore, efficiently utilizing the content of technical specifications to address the complex operating conditions of nuclear power plants has become a pressing technical problem. Summary of the Invention

[0004] The main objective of this application is to propose an anomaly handling method, system, and related media based on nuclear power plant technical specifications, aiming to achieve efficient utilization of technical specifications.

[0005] To achieve the above objectives, a first aspect of this application proposes an anomaly handling method based on nuclear power plant technical specifications, the method comprising:

[0006] Acquire real-time operating data of a nuclear power plant; wherein the real-time operating data is configured with system identification information that identifies the source of the data;

[0007] Based on the real-time operational data, the target operating mode of the nuclear power plant is determined;

[0008] Based on the system identification information and the target operation mode, an applicable target rule group is matched from a plurality of preset event rule groups; wherein, the event rule group is constructed based on the content of the technical specification.

[0009] Based on the target rule group, anomalies are identified in the real-time running data, and target processing information corresponding to the triggered target event rule is obtained.

[0010] In some embodiments, before matching an applicable target rule group from a plurality of preset event rule groups based on the system identification information and the target operating mode, the method further includes constructing the event rule group based on the technical specifications, specifically including:

[0011] The technical specifications are processed by character recognition to obtain the text data of the specifications.

[0012] Based on the textual data in the specification, the scope of application is parsed to obtain multiple rules and their applicable conditions.

[0013] The event rule data is obtained by parsing the text data in the specification document.

[0014] The event rule data is grouped based on the applicable conditions information of each rule to obtain the corresponding event rule group.

[0015] In some embodiments, the step of parsing rule events based on the textual data of the specification to obtain event rule data includes:

[0016] Based on the text data in the specification document, the text content is parsed to obtain the operating constraints, solution plan information, event handling time, and anomaly check frequency; wherein, the event handling time represents the time allocated to resolve anomalies, and the anomaly check frequency represents the frequency of proactively checking the equipment or functions.

[0017] Anomaly detection conditions are generated based on the aforementioned operational constraints;

[0018] An anomaly handling plan corresponding to the operational constraints is generated based on the aforementioned measures and plans information;

[0019] Anomaly event rules are constructed based on the anomaly determination conditions, the anomaly handling scheme, the event handling time, and the anomaly checking frequency.

[0020] All the aforementioned abnormal event rules are integrated to obtain event rule data.

[0021] In some embodiments, each triggered target event rule is converted into an abnormal trigger event and stored in an event record database to obtain historical event data. Based on the target rule group, anomaly identification is performed on the real-time running data, and target processing information corresponding to the triggered target event rule is obtained, including:

[0022] Based on the target rule group, the association range is determined in the historical event data to obtain the potential association event group;

[0023] Anomalies are identified based on the real-time operational data to obtain the triggered target event rules and generate corresponding target trigger events.

[0024] Based on the target triggering event, perform association matching in the potential associated event group to obtain multiple target associated events;

[0025] Based on the target triggering event and multiple target-related events, the corresponding target processing information is obtained.

[0026] In some embodiments, association matching is performed on the potential associated event group based on the target triggering event to obtain multiple target associated events, including:

[0027] By using the target event rules corresponding to the target triggering event, the event triggering factors and event triggering time corresponding to the target triggering event are determined;

[0028] Based on the event triggering factors and the event triggering time, multiple target related events are identified from the potential related event group.

[0029] In some embodiments, before performing association matching based on the target triggering event in the group of potential associated events to obtain multiple target associated events, the method further includes constructing an event association network, specifically including:

[0030] Obtain the event triggering time and event triggering factors corresponding to each of the aforementioned abnormal triggering events;

[0031] Based on the event triggering time and the event triggering factors, a knowledge graph is constructed for each of the corresponding abnormal triggering events to obtain an event association network.

[0032] In some embodiments, after obtaining the corresponding target processing information based on the target triggering event and multiple target-related events, the method further includes:

[0033] Based on the triggered target event rules, generate the target triggered event configured with an event priority label, event trigger time, and event processing time;

[0034] The severity rating of the target triggered event is obtained by rating the severity of the event based on the event priority label, the event trigger time, and the event processing time.

[0035] Based on the event urgency rating and multiple target-related events, a urgency level rating is obtained to arrive at a comprehensive urgency rating.

[0036] An event warning is issued based on the comprehensive crisis rating.

[0037] In some embodiments, after identifying anomalies in the real-time running data based on the target rule group and obtaining target processing information corresponding to the triggered target event rule, the method further includes evaluating the target processing information, specifically including:

[0038] Based on the target processing information, anomaly repair processing is performed on the target triggering event that triggers the target event rule to obtain anomaly repair results;

[0039] An anomaly repair evaluation is performed based on the anomaly repair results to obtain rule feedback information;

[0040] The target event rule is iterated based on the rule feedback information to obtain the target modification rule.

[0041] In some embodiments, iterating the target event rule based on the rule feedback information to obtain the target modification rule includes:

[0042] Based on the target modification rules, a simulated environment test is performed to obtain rule test information;

[0043] Based on the rule test information, add the target modification rule to the target rule group.

[0044] In some embodiments, when determining the target operating mode of the nuclear power plant based on the real-time operating data, and when the nuclear power plant needs to switch operating modes, the method further includes:

[0045] Receive an operation mode switching request and obtain the operation mode to be switched to;

[0046] Based on the operating mode to be switched, a suitable preliminary rule group is matched from a set of preset event rule groups;

[0047] An anomaly correlation assessment is performed based on the preparatory rule group and the triggered target event rules to obtain mode switching suggestions;

[0048] Execute the operation mode switching request according to the mode switching suggestion.

[0049] In some embodiments, the step of performing anomaly correlation evaluation based on the pre-set rule group and the triggered target event rule to obtain mode switching suggestions includes:

[0050] If the abnormal event rules included in the preparatory rule group are related to the already triggered target event rule, a mode switching suggestion that does not recommend switching the operating mode is generated.

[0051] If the abnormal event rules included in the preparatory rule group are not associated with the triggered target event rules, a mode switching suggestion is generated to recommend switching the operating mode.

[0052] To achieve the above objectives, a second aspect of this application proposes an anomaly handling system based on nuclear power plant technical specifications, the system comprising a data acquisition module, a rule application scope determination module, an operation mode identification module, and an anomaly identification and processing module;

[0053] The data acquisition module is used to acquire real-time operating data of the nuclear power plant; wherein, the real-time operating data is configured with system identification information that identifies the source of the data;

[0054] The operation mode identification module is used to determine the target operation mode of the nuclear power plant based on the real-time operation data.

[0055] The rule scope determination module is used to match an applicable target rule group from a set of preset event rule groups based on the system identification information and the target operation mode; wherein, the event rule group is constructed based on the content of the technical specification.

[0056] The anomaly identification and processing module is used to identify anomalies in the real-time running data based on the target rule group, and to obtain target processing information corresponding to the target event rule according to the triggered target event rule.

[0057] In some embodiments, the system further includes a rules database and an event log database;

[0058] The rule database is used to store the abnormal event rules in the event rule group;

[0059] The event record database is used to store abnormal triggering events transformed according to the triggered target event rules, and to record the event priority tag, event trigger time and event processing time corresponding to the target event rules.

[0060] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the exception handling method based on the nuclear power plant technical specifications described in the first aspect.

[0061] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the exception handling method based on the nuclear power plant technical specifications described in the first aspect.

[0062] This application proposes an anomaly handling method, system, and related media based on nuclear power plant technical specifications. It acquires real-time operational data from the nuclear power plant and identifies the source of this data using system identification information. The system determines the current target operating mode of the nuclear power plant based on the real-time operational data. Then, using the system identification information and the target operating mode, it matches an applicable target rule group from multiple event rule groups constructed based on the technical specifications. Anomalies are identified in the real-time operational data using these target rule groups. When an anomaly is detected, the corresponding target event rule is triggered, and corresponding target processing information is obtained based on the target event rule. Therefore, this application constructs multiple event rule groups based on the technical specifications, determines the applicable target rule group based on the system identification information and the current target operating mode of the nuclear power plant, and then identifies anomalies in the actual operational data of the nuclear power plant. When a target event rule is triggered, corresponding target processing information is provided. This achieves efficient utilization of the technical specifications. Attached Figure Description

[0063] Figure 1 This is a flowchart of an anomaly handling method based on nuclear power plant technical specifications provided in an embodiment of this application;

[0064] Figure 2 yes Figure 1 The flowchart preceding step S103;

[0065] Figure 3 yes Figure 2 The flowchart of step S203 in the process;

[0066] Figure 4 yes Figure 1 The flowchart of step S104 in the process;

[0067] Figure 5 yes Figure 4 The flowchart of step S403 in the process;

[0068] Figure 6 yes Figure 4 The flowchart preceding step S403;

[0069] Figure 7 yes Figure 4 The flowchart following step S404;

[0070] Figure 8 yes Figure 1 The flowchart following step S104;

[0071] Figure 9 yes Figure 8 The flowchart of step S803 in the process;

[0072] Figure 10 This is another flowchart of the anomaly handling method based on nuclear power plant technical specifications provided in the embodiments of this application;

[0073] Figure 11 yes Figure 10 The flowchart of step S1003 in the process;

[0074] Figure 12 This is a schematic diagram of the structure of an anomaly handling system based on nuclear power plant technical specifications provided in an embodiment of this application;

[0075] Figure 13 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0077] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0079] This application provides an anomaly handling method, system, and related media based on nuclear power plant technical specifications, aiming to achieve efficient utilization of technical specifications.

[0080] The anomaly handling method, system, and related media based on nuclear power plant technical specifications provided in this application are specifically illustrated through the following embodiments. First, the anomaly handling method based on nuclear power plant technical specifications in this application embodiment is described.

[0081] The anomaly handling method based on nuclear power plant technical specifications provided in this application relates to the field of nuclear power plant operation and management technology. This anomaly handling method based on nuclear power plant technical specifications can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the anomaly handling method based on nuclear power plant technical specifications, but is not limited to the above forms.

[0082] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0083] Figure 1 This is an optional flowchart of an anomaly handling method based on nuclear power plant technical specifications provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0084] Step S101: Obtain real-time operating data of the nuclear power plant; wherein, the real-time operating data is configured with system identification information that identifies the source of the data;

[0085] Step S102: Based on real-time operating data, determine the target operating mode of the nuclear power plant;

[0086] Step S103: Based on the system identification information and the target operation mode, match the applicable target rule group from multiple preset event rule groups; wherein, the event rule group is constructed based on the content of the technical specification.

[0087] Step S104: Based on the target rule group, perform anomaly identification on the real-time running data, and obtain the target processing information of the corresponding target event rule according to the triggered target event rule.

[0088] Steps S101 to S104 of this application embodiment involve acquiring real-time operational data from a nuclear power plant and using system identification information to identify the source of the real-time operational data. The current target operating mode of the nuclear power plant is determined based on the real-time operational data. Then, using the system identification information and the target operating mode, an applicable target rule group is matched from multiple event rule groups constructed based on the technical specifications. Anomalies are identified in the real-time operational data using the target rule groups. When an anomaly is detected, the corresponding target event rule is triggered, and corresponding target processing information can be obtained based on the target event rule. Therefore, this application constructs multiple event rule groups based on the technical specifications, determines the applicable target rule group based on the system identification information and the current target operating mode of the nuclear power plant, and then identifies anomalies in the actual operational data of the nuclear power plant. When a target event rule is triggered, corresponding target processing information is provided. This achieves efficient utilization of the technical specifications.

[0089] In step S101 of some embodiments, real-time operating data of the nuclear power plant is acquired. Real-time operating data typically includes various parameters and indicators, such as temperature, pressure, flow rate, and vibration, which are key information for assessing the current state of the nuclear power plant. To ensure the accuracy and reliability of the data, real-time operating data includes system identification information, which identifies the source of the data.

[0090] The configuration of system identification information allows data to be traced back to a specific system or device, facilitating subsequent processing. For example, if a sensor detects an abnormal reading, system identification information can help operators quickly locate the specific sensor, determine whether the reading is accurate, or whether there is a device malfunction. Furthermore, system identification information helps distinguish between multiple similar systems or devices, especially in large nuclear power plants where multiple identical sensors or systems may be operating simultaneously.

[0091] This step allows for real-time monitoring of the nuclear power plant's operational status, providing essential data support for subsequent abnormal event rule matching and anomaly identification. This real-time data acquisition is crucial for achieving automated and intelligent nuclear power plant operation management, enabling the dynamic application of technical specifications to actual operation and improving the plant's operational efficiency and safety.

[0092] In step S102 of some embodiments, during the operation of a nuclear power plant, there are multiple different operating modes, each with its specific operating parameters and requirements. These modes may include various states such as startup, power operation, maintenance, and shutdown, each of which has different impacts on the equipment and systems of the nuclear power plant.

[0093] To ensure the safe and efficient operation of nuclear power plants, it is essential to monitor and accurately determine their current operating mode in real time. Real-time operational data includes the current status information of various systems and equipment within the nuclear power plant, such as key parameters like temperature, pressure, and flow rate. In-depth analysis of this data allows for the identification of the nuclear power plant's current state and its matching with preset operating modes, thereby determining the target operating mode for the current nuclear power plant.

[0094] In step S103 of some embodiments, the system analyzes the system identification information and the target operating mode of the nuclear power plant in the real-time operating data. Then, based on the system identification information and the target operating mode, it matches the most suitable target rule group from multiple event rule groups to the real-time operating data. Through techniques such as exact matching or pattern matching, the system can quickly find and apply the appropriate target rule group. The event rule group is pre-constructed according to the content of the technical specifications. The event rule group contains several abnormal event rules, which represent the rules and conditions that the nuclear power plant should follow during operation.

[0095] Technical specifications detail the operational constraints that nuclear power plants must meet under various operating modes, including equipment performance parameters, safety thresholds, and operating procedures. By transforming these operational constraints into rules for abnormal events, a framework for analyzing and evaluating real-time data can be provided.

[0096] This matching mechanism makes nuclear power plant monitoring systems more flexible and efficient. It allows the system to dynamically select the most appropriate anomaly rules for analysis based on the characteristics of real-time data, thereby improving monitoring accuracy and response speed. At the same time, it reduces the workload of operators, as they can rely on the system to automatically match and apply anomaly rules without having to manually search for and apply complex information from technical specifications.

[0097] In step S104 of some embodiments, after the target rule group is determined, anomaly identification is performed on the real-time operating data of the nuclear power plant. In the complex operating environment of a nuclear power plant, the real-time monitoring system must be able to accurately identify any deviations from normal operating parameters in order to take timely measures to ensure the safe and stable operation of the power plant.

[0098] The target rule set contains various operational constraints and countermeasures specified in the technical specifications, serving as the basis for assessing whether real-time operational data meets safety standards. By comparing real-time operational data with the abnormal event rules in the target rule set, the system can identify any anomalies, such as a parameter exceeding a set safety threshold or the equipment operating state not conforming to the expected pattern.

[0099] Once an anomaly is identified, the system will provide corresponding response measures under the operational constraints specified in the technical specifications, based on the triggered target event rules. This automated response mechanism not only improves the efficiency of handling anomalies but also reduces the possibility of human error.

[0100] In some embodiments, this application utilizes rule engine technology to construct event rule groups. A rule engine is a software component designed to handle complex business logic and decision-making. The primary purpose of a rule engine is to separate event rules from application code for easier management and maintenance. Nuclear power plant operating parameters and safety regulations may need to be updated according to the latest technological and regulatory requirements; the flexibility of a rule engine makes these updates easier to implement. Nuclear power plant technical specifications record numerous operational constraints and corresponding measures. Using a rule engine, abnormal event rules can be added, deleted, or modified very flexibly. Furthermore, rule engines are designed to handle the growth in the number of rules, thus easily processing hundreds or thousands of rules. Advanced rule engines typically include performance optimization mechanisms such as caching, indexing, and inference mechanisms, which are crucial for handling large numbers of rules. Therefore, rule engines can handle the large number of abnormal event rules derived from technical specifications very well. On the other hand, there are complex operating modes and functional interactions between systems and equipment in nuclear power plants. Rule engines support functions such as conflict detection, rule inference, and simulated execution, which can effectively solve these problems and handle application scenarios containing numerous conditional judgments and complex logic. In some embodiments, rule engines and logic diagrams can be combined. The rule engine handles complex business logic, particularly rules for exceptional events that require frequent updates and maintenance. The logic diagram visualizes the rule flow within the rule engine, helping users better understand its behavior. Logic diagrams can be used to represent the decision-making process within the rule engine. For example, a decision tree logic diagram can be created to show which actions should be taken under different conditions. In this way, the rule engine ensures the accuracy and efficiency of its logical processing, while the logic diagram provides an intuitive view, helping non-technical personnel better understand how the rule engine works, thereby enhancing system transparency and usability.

[0101] Please see Figure 2In some embodiments, step S103 is preceded by constructing an event rule group based on the technical specifications, which may include, but is not limited to, steps S201 to S204:

[0102] Step S201: Perform character recognition processing on the content recorded in the technical specifications to obtain the text data of the specifications;

[0103] Step S202: Analyze the applicable scope based on the text data in the specification to obtain multiple rules applicable conditions information;

[0104] Step S203: Parse the rule events based on the text data in the specification to obtain the event rule data;

[0105] Step S204: Group the event rule data based on the applicable conditions information of each rule to obtain the corresponding event rule group.

[0106] In step S201 of some embodiments, character recognition processing is performed on the original content of the technical specification. This typically refers to converting a paper document or specification in PDF format into editable and analyzable text data. This can be achieved using optical character recognition technology or other language modeling techniques.

[0107] In step S202 of some embodiments, multiple rule application condition information are extracted by parsing the obtained specification text data. In the technical specifications, the rule application condition information defines the specific conditions under which operational constraints or countermeasures apply, such as specific operating modes, equipment states, or environmental parameters. This step clarifies which rules should be triggered under what circumstances.

[0108] In step S203 of some embodiments, the textual data in the specification is further analyzed to identify and construct specific anomaly rules. Anomaly rules are constraint rules derived from running constraint transformations; they define the actions the nuclear power plant must take when certain conditions are detected. This step transforms the textual descriptions in the specification into structured anomaly rule data, providing clear guidance for subsequent real-time monitoring.

[0109] In step S204 of some embodiments, based on the previously extracted rule applicability information, the abnormal event rules in the event rule data are grouped to form multiple event rule groups. Each event rule group contains a series of abnormal event rules that should be considered and applied under specific conditions. This grouping process enables the system to quickly match and apply the correct abnormal event rules during real-time monitoring, thereby improving the efficiency of anomaly identification and response.

[0110] Steps S201 to S204 transform complex textual information from technical specifications into abnormal event rules that can be used for real-time monitoring, providing strong support for the safe operation of nuclear power plants. This method not only improves the accuracy of abnormal event rule application but also significantly enhances the automation and intelligence level of nuclear power plant management based on technical specifications.

[0111] Please see Figure 3 In some embodiments, step S203 may include, but is not limited to, steps S301 to S305:

[0112] Step S301: Based on the text data in the specification document, the text content is parsed to obtain the operating constraints, solution information, event handling time, and anomaly check frequency; where the event handling time represents the time allotted to resolve the anomaly, and the anomaly check frequency represents the frequency of proactively checking the equipment or function.

[0113] Step S302: Generate exception judgment conditions based on the operating constraints;

[0114] Step S303: Generate an anomaly handling plan for the corresponding operational constraints based on the measures plan information;

[0115] Step S304: Construct abnormal event rules based on abnormal judgment conditions, abnormal handling schemes, event handling time, and abnormal inspection frequency;

[0116] Step S305: Integrate all abnormal event rules to obtain event rule data.

[0117] In step S301 of some embodiments, important operating parameters are extracted by parsing the text content of the specification document, including operating constraints, mitigation plan information, incident handling time, and anomaly inspection frequency. Operating constraints are technical parameters that must be followed for the safe operation of a nuclear power plant, while mitigation plan information specifies the countermeasures to be taken in specific abnormal situations. Incident handling time is the allowed time window for resolving abnormal problems, and the anomaly inspection frequency determines the cycle of proactive inspections of equipment or systems.

[0118] In step S302 of some embodiments, anomaly detection conditions for real-time monitoring are generated based on the extracted operational constraints. These conditions define under what parameters or states the system should consider an anomaly to exist and trigger an alarm.

[0119] In step S303 of some embodiments, a corresponding anomaly handling plan is generated for each operational constraint based on the previously obtained mitigation plan information. That is, an anomaly handling plan corresponds to each anomaly determination condition. These plans include specific operational steps to be performed when an anomaly is detected, to ensure that the nuclear power plant can respond promptly and take appropriate measures.

[0120] In step S304 of some embodiments, anomaly event rules are constructed by combining anomaly determination conditions, anomaly handling schemes, event handling times, and anomaly checking frequencies. This step integrates the previously separated information into complete anomaly event rules. Each anomaly event rule includes not only the conditions for determining an anomaly, but also a detailed scheme for how to respond once an anomaly occurs.

[0121] In step S305 of some embodiments, all constructed anomaly event rules are integrated to form event rule data. This event rule data will then be further grouped and processed according to the rule applicability information. This provides the real-time monitoring system with a comprehensive rule base, enabling the system to quickly and accurately identify anomalies and take predefined measures under different target operating modes and conditions.

[0122] In some embodiments, operators can also customize anomaly judgment conditions, anomaly handling schemes, event handling times, and anomaly check frequencies by configuring the input area for creating anomaly event rules through system configuration. This means that operators can set specific parameters or thresholds based on the anomaly event rules given in the technical specifications, according to the specific needs and operating experience of the nuclear power plant, to flexibly adjust and optimize the monitoring and handling process of anomalies, thereby improving overall operating efficiency and safety performance. Furthermore, new anomaly event rules, more suitable for detecting and judging anomalies, can be created based on some highly correlated anomalies in the technical specifications.

[0123] In some embodiments, the operator can also configure a control area for testing abnormal event rules through the system to test whether the triggering and operation of abnormal event rules are normal, such as whether the state cause of the abnormal triggering event is correctly described and whether the event processing time is reasonable.

[0124] In some embodiments, the operator also has the authority to eliminate or reset an abnormal triggering event that has already occurred, in order to eliminate or reset an abnormal triggering event that was erroneously triggered or tested.

[0125] In some embodiments, since abnormal event rules are grouped based on system identification information, relevant personnel can query the active abnormal event rules based on device information, or manage the abnormal event rules.

[0126] Please see Figure 4In some embodiments, each triggered target event rule is converted into an abnormal trigger event and stored in the event record database to obtain historical event data. Step S104 may include, but is not limited to, steps S401 to S404:

[0127] Step S401: Based on the target rule group, determine the association range in historical event data to obtain the potential association event group;

[0128] Step S402: Based on real-time running data, perform anomaly identification, obtain the triggered target event rules, and generate the corresponding target trigger event;

[0129] Step S403: Based on the target triggering event, perform association matching in the potential associated event group to obtain multiple target associated events;

[0130] Step S404: Based on the target triggering event and multiple target-related events, obtain the corresponding target processing information.

[0131] In step S401 of some embodiments, a potential correlation range is first determined in historical event data based on the target rule group to identify other events that may be related to the current anomaly. This step obtains a group of potentially related events by analyzing historical data to find events that are similar to or causally related to the current anomaly conditions. This helps the system understand the overall picture of the anomaly, rather than just looking at individual events in isolation.

[0132] In step S402 of some embodiments, anomaly identification is performed using real-time runtime data. During this process, the system obtains the triggered target event rule based on the anomaly event rules in the target rule group. When a target event rule is triggered, the system generates a corresponding target trigger event, which records the specific details of the anomaly and the triggering conditions. The target trigger event and the anomaly trigger event are essentially the same, except that the anomaly trigger event is historical data stored in the event record database.

[0133] In step S403 of some embodiments, the system performs in-depth correlation matching within the previously identified group of potentially related events based on the target triggering event. This step aims to discover other events related to the current anomaly, whether these events are direct causes or indirect influences. In this way, the system can identify multiple target-related events, providing more information for a comprehensive understanding and handling of the anomaly.

[0134] In step S404 of some embodiments, the system comprehensively considers the target triggering event and multiple identified target-related events to determine the optimal handling scheme. This step involves in-depth analysis of the events and evaluation of possible handling measures to obtain corresponding target handling information. This information will guide the operator to take appropriate actions to resolve the anomaly and restore the normal operation of the system.

[0135] Steps S401 to S404 constitute a comprehensive and in-depth anomaly handling process. This process not only responds to anomalies in real time but also provides a more comprehensive perspective and more effective handling solutions by analyzing historical data and identifying related events. This not only improves the safety and reliability of nuclear power plant operations but also optimizes the efficiency and effectiveness of incident handling.

[0136] Please see Figure 5 In some embodiments, step S403 may include, but is not limited to, steps S501 to S502:

[0137] Step S501: Determine the event triggering factors and event triggering time of the corresponding target triggering event through the target event rules corresponding to the target triggering event;

[0138] Step S502: Identify multiple target related events from the potential related event group based on event triggering factors and event triggering time.

[0139] In step S501 of some embodiments, the event triggering factor and triggering time of the triggering event are determined according to the target event rule corresponding to the target triggering event. The event triggering factor refers to the specific condition or parameter that causes the rule to be triggered, such as a sensor reading exceeding a preset safety threshold. The event triggering time is the specific moment when the target event rule is triggered, which is very important for understanding the timing and dynamic changes of the target triggering event.

[0140] In step S502 of some embodiments, target-related events that are highly relevant to the current target triggering event are filtered from the potential related event group based on the event triggering factors and event triggering time. This filtering process may involve comparing timestamps, matching event types, and analyzing the source of anomalies. In this way, the system can identify target-related events that are closely related to the current target triggering event in time and logic.

[0141] The significance of this step lies in its ability to not only help operators quickly pinpoint the root cause of an anomaly but also predict potential cascading effects and secondary consequences. For example, if a device malfunction triggers a series of protective actions, these actions may also be considered events associated with the initial failure. By identifying these related events, operators can more comprehensively assess the impact of the anomaly and take appropriate measures to avoid potential risks.

[0142] Please see Figure 6 In some embodiments, steps S601 to S602 may be included before step S403:

[0143] Step S601: Obtain the event triggering time and event triggering factors corresponding to each abnormal triggering event;

[0144] Step S602: Based on the event triggering time and event triggering factors, construct a knowledge graph for each corresponding abnormal triggering event to obtain an event association network.

[0145] In steps S601 to S602 of some embodiments, the system constructs an event association network based on historical event data, linking various abnormal triggering events with related factors in chronological order. This can be achieved by constructing a knowledge graph to associate the triggering factors and triggering time of each abnormal triggering event, thus building the event association network. This better helps operators trace past abnormal triggering events and also helps identify target related events from potentially related event groups.

[0146] In some embodiments, the associated target related events can also be determined from the potential related event group based on the system identification information configured by the real-time running data that triggers the target trigger event, or the functional modules involved in the target trigger event, so as to more comprehensively evaluate the abnormal source and development process of the abnormal trigger event.

[0147] Steps S501 and S502 constitute an effective event correlation analysis process. This process provides strong support for anomaly handling in nuclear power plants by deeply analyzing the triggering factors and timing of anomalies and accurately matching them among potentially related events. This not only improves the accuracy and efficiency of anomaly handling but also enhances the safety and reliability of the entire system.

[0148] Please see Figure 7 In some embodiments, step S404 may be followed by steps S701 to S704, including but not limited to:

[0149] Step S701: Generate a target trigger event configured with an event priority label, event trigger time, and event processing time according to the triggered target event rules;

[0150] Step S702: Based on the event priority label, event trigger time, and event processing time, the severity level is rated to obtain the event severity rating of the target triggering event;

[0151] Step S703: Based on the event urgency rating and multiple target-related events, a urgency level rating is determined to obtain a comprehensive urgency rating;

[0152] Step S704: Issue an event warning based on the comprehensive urgency rating.

[0153] In step S701 of some embodiments, a target triggering event is generated based on the triggered target event rule, and the event triggering event is configured with an event priority label, an event trigger time, and an event processing time. The event priority label is a key parameter that categorizes events according to their severity and urgency. The event trigger time records the specific moment when the rule is triggered, while the event processing time is the allowed time window for resolving the anomaly.

[0154] In step S702 of some embodiments, a severity rating is performed based on the event priority label, event trigger time, and event processing time to determine the urgency and potential impact of the event, thus obtaining an event severity rating for the target triggering event. For example, in some embodiments, if insufficient event processing may lead to adverse effects, its severity rating will be very high, meaning that immediate action is required to respond.

[0155] In step S703 of some embodiments, this rating process is further extended to multiple target-related events to obtain a comprehensive hazard rating. In this step, the system comprehensively considers all related events, including their interactions and potential cascading effects, to arrive at a comprehensive hazard rating. This rating considers not only the severity of individual events but also their potential impact as a whole on the operational safety of the nuclear power plant.

[0156] In step S704 of some embodiments, the system issues an event warning based on a comprehensive urgency rating. This warning provides details of the event, including its urgency level, potential impact, and recommended response measures. The warning can be disseminated through various channels, including control room alarm systems, email, SMS, or automated voice calls, ensuring that all relevant personnel receive timely notification and take necessary actions.

[0157] Through steps S701 to S704, the nuclear power plant's safety monitoring system can respond quickly and accurately to abnormal events, ensuring that all relevant personnel are promptly informed of the situation and appropriate response measures. This not only improves the safety and reliability of the nuclear power plant but also enhances the efficiency and effectiveness of responding to emergencies.

[0158] Please see Figure 8 In some embodiments, step S104 is followed by an evaluation of the target processing information, which may include, but is not limited to, steps S801 to S803:

[0159] Step S801: Based on the target processing information, perform anomaly repair processing on the target triggering event of the triggering target event rule to obtain the anomaly repair result;

[0160] Step S802: Based on the anomaly repair results, perform anomaly repair evaluation to obtain rule feedback information;

[0161] Step S803: Iterate the target event rules based on the rule feedback information to obtain the target modification rules.

[0162] In step S801 of some embodiments, anomaly repair processing is performed on the target triggering event of the triggering target event rule based on the target processing information. In this step, the operator or automated system will execute the countermeasures given in the target processing information to resolve or mitigate the abnormal event. These countermeasures may include adjusting equipment parameters, restarting the system, executing specific maintenance procedures, etc. After the countermeasures are executed, the system will record the anomaly repair results, including whether the processing was successful or not, the effect of the processing, and any observed side effects.

[0163] In step S802 of some embodiments, the system performs an anomaly repair evaluation based on the anomaly repair results. This evaluation process involves analyzing the effectiveness of the anomaly repair handling, determining whether the problem was successfully resolved, and whether the expected goals were achieved. The evaluation results will generate rule feedback information, which is crucial for understanding the effectiveness and efficiency of the existing target event rules.

[0164] In step S803 of some embodiments, the system iterates over the target event rules based on rule feedback information. This iterative process may include adjusting anomaly detection conditions, modifying processing schemes, updating event processing times, or adjusting anomaly check frequencies. In this way, nuclear power plants can continuously learn and improve, making event rules more accurate and effective.

[0165] Steps S801 to S803 collectively achieve a continuous improvement cycle based on experience feedback. By evaluating the results of anomaly repairs and updating event rules accordingly, nuclear power plants can improve their ability to respond to anomaly-triggered events, reduce the probability of similar future events, and enhance overall operational safety and reliability. This self-optimization and learning capability is a key feature of modern nuclear power plant management, helping to ensure that nuclear power plants maintain the highest safety standards under constantly changing operating conditions.

[0166] Please see Figure 9 In some embodiments, step S803 may include, but is not limited to, steps S901 to S902:

[0167] Step S901: Modify the rules according to the target and conduct a simulated environment test to obtain rule test information;

[0168] Step S902: Based on the rule test information, add the target modification rule to the target rule group.

[0169] In step S901 of some embodiments, the target modification rule needs to undergo thorough testing and verification before being applied to actual operation. The target modification rule is first tested in a simulation environment. A simulation environment is a low-risk testing platform that can simulate the actual operating conditions of a nuclear power plant, allowing operators or engineers to evaluate the effectiveness of the target modification rule without interfering with the real system. In the simulation test, various hypothetical scenarios can be created, including equipment failures and parameter anomalies, to verify whether the target modification rule can be correctly triggered and guide appropriate handling measures. In this way, the accuracy, reliability, and efficiency of the target modification rule can be verified, ensuring that the target modification rule can effectively identify and respond to abnormal events in practical applications.

[0170] In step S902 of some embodiments, after the simulated environment test is completed, the target modification rule is added to the target rule group based on the rule test information. This step integrates the validated rules into the nuclear power plant's formal monitoring system. After the target modification rules are added to the target rule group, they will begin to function in the actual monitoring process, guiding operators or automated systems to respond to abnormal events.

[0171] Through steps S901 and S902, the nuclear power plant can ensure that all target modification rules have undergone rigorous testing and verification, thereby guaranteeing the continuity and safety of the plant's operation. This rigorous testing and review process helps reduce potential risks caused by errors in target modification rules, enhances the nuclear power plant's response to abnormal events, and improves overall operational efficiency and safety.

[0172] Please see Figure 10In some embodiments, when a nuclear power plant needs to switch operating modes, the method may also include, but is not limited to, steps S1001 to S1004:

[0173] Step S1001: Receive an operation mode switching request and obtain the operation mode to be switched.

[0174] Step S1002: Based on the operating mode to be switched, match the applicable preparatory rule group from multiple preset event rule groups;

[0175] Step S1003: Based on the preparatory rule group and the triggered target event rules, perform anomaly correlation evaluation to obtain mode switching suggestions;

[0176] Step S1004: Execute the operation mode switching request according to the mode switching suggestion.

[0177] In step S1001 of some embodiments, when a nuclear power plant needs to switch operating modes, the plant's control system receives an operating mode switching request. This request may originate from an operator or be determined by an automated system. The request specifies the operating mode to which the plant is currently preparing to switch, referring to the operating mode to which the request is made.

[0178] In step S1002 of some embodiments, the system matches an applicable preliminary rule group from a plurality of preset event rule groups based on the operating mode to be switched. The preliminary rule group contains exception event rules and parameters corresponding to the operating mode to be switched, and these exception event rules define how the system should respond to different exception events in the new operating mode.

[0179] In step S1003 of some embodiments, the system performs anomaly correlation evaluation based on the preparatory rule group and the already triggered target event rules. This step is to query whether there are any abnormal triggering events among the already triggered target event rules that will affect the switched operating mode, predict possible abnormal situations after the switching operating mode, and generate mode switching suggestions, suggesting whether to respond to the operating mode switching request and switch the operating mode.

[0180] In step S1004 of some embodiments, the control system executes an operating mode switching request based on the mode switching suggestion. This step includes operations such as updating system parameters, adjusting equipment settings, and notifying relevant personnel to ensure a smooth and safe switching process.

[0181] Please see Figure 11 In some embodiments, step S1003 may include, but is not limited to, steps S1101 to S1102:

[0182] Step S1101: If the abnormal event rules included in the preparatory rule group are related to the triggered target event rules, generate a mode switching suggestion that does not recommend switching the operating mode.

[0183] Step S1102: If the abnormal event rules included in the preparatory rule group are not related to the triggered target event rules, generate a mode switching suggestion to recommend switching the operating mode.

[0184] In step S1101 of some embodiments, if an abnormal event rule included in the preparatory rule group is associated with a target event rule that has already been triggered, the system generates a mode switching suggestion that does not recommend switching operating modes. This association may mean that the current abnormal event has not been properly handled, and switching to the operating mode to be switched may exacerbate the existing problem. For example, if an abnormal event related to the cooling system is occurring, and the new operating mode requires higher cooling efficiency, then switching may increase the pressure on the cooling system. In this case, it is recommended not to switch until the current abnormal event is resolved.

[0185] In step S1102 of some embodiments, if the abnormal event rule in the prepared rule group is not associated with the triggered target event rule, it indicates that switching to the new operating mode is unlikely to be affected by the current abnormal event. In this case, the system generates a mode switching suggestion recommending switching operating modes. This may be because the current abnormal event has been isolated or resolved, or the new operating mode has sufficient buffer to handle potential problems.

[0186] Through steps S1101 to S1102, the nuclear power plant can carefully analyze the correlation between the preliminary rule set and the triggered target event rules, taking into account existing triggered target event rules, to determine whether to switch operating modes. This helps ensure the smoothness and safety of nuclear power plant operating mode switching and prevents potential risks and problems.

[0187] This application's embodiments acquire real-time operational data from a nuclear power plant and identify the source of this data using system identification information. The current target operating mode of the nuclear power plant is determined based on the real-time operational data. Then, using the system identification information and the target operating mode, an applicable target rule group is matched from multiple event rule groups constructed based on the technical specifications. Anomalies are identified in the real-time operational data using the target rule groups. When an anomaly is detected, the corresponding target event rule is triggered, and corresponding target processing information can be obtained based on the target event rule. Therefore, this application constructs multiple event rule groups based on the technical specifications, determines the applicable target rule group based on the system identification information and the current target operating mode of the nuclear power plant, and then identifies anomalies in the nuclear power plant's actual operational data. When a target event rule is triggered, corresponding target processing information is provided. This achieves efficient utilization of the technical specifications.

[0188] Please see Figure 12 This application also provides an anomaly handling system based on nuclear power plant technical specifications, which can implement the above-mentioned anomaly handling method based on nuclear power plant technical specifications. The system includes a data acquisition module, a rule application scope determination module, an operation mode identification module, and an anomaly identification and processing module.

[0189] The data acquisition module is used to acquire real-time operating data of the nuclear power plant; the real-time operating data is configured with system identification information that identifies the source of the data.

[0190] The operation mode recognition module is used to determine the target operation mode of the nuclear power plant based on real-time operation data;

[0191] The rule scope determination module is used to match the applicable target rule group from multiple preset event rule groups based on system identification information and target operation mode; wherein, the event rule group is constructed based on the content of the technical specification.

[0192] The anomaly identification and processing module is used to identify anomalies in real-time running data based on target rule groups, and to obtain the target processing information of the corresponding target event rule according to the triggered target event rule.

[0193] The specific implementation of the anomaly handling system based on nuclear power plant technical specifications is basically the same as the specific implementation of the anomaly handling method based on nuclear power plant technical specifications described above, and will not be repeated here.

[0194] Please see Figure 12In some embodiments, the system further includes a rule database and an event log database. The rule database stores abnormal event rules in the event rule group. The event log database stores abnormal triggering events transformed according to the triggered target event rules, and records the event priority label, event trigger time, and event processing time of the corresponding target event rule.

[0195] Operators can use the system's configured historical query module to export abnormal trigger events stored in the event log database, or sort or query them according to certain criteria, and then display them. Abnormal trigger events are events generated based on the rules that trigger the target event.

[0196] In some embodiments, the operator can display the target triggering events and corresponding target processing information that have occurred on a terminal, thereby facilitating timely response and monitoring by relevant staff.

[0197] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described exception handling method based on the nuclear power plant technical specifications. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0198] Please see Figure 13 , Figure 13 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0199] The processor 1301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0200] The memory 1302 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1302 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1302 and is called and executed by the processor 1301 to execute the exception handling method based on the nuclear power plant technical specifications of the embodiments of this application.

[0201] The input / output interface 1303 is used to implement information input and output;

[0202] The communication interface 1304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0203] Bus 1305 transmits information between various components of the device (e.g., processor 1301, memory 1302, input / output interface 1303, and communication interface 1304);

[0204] The processor 1301, memory 1302, input / output interface 1303 and communication interface 1304 are connected to each other within the device via bus 1305.

[0205] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described exception handling method based on nuclear power plant technical specifications.

[0206] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0207] The anomaly handling method, system, and related media based on nuclear power plant technical specifications provided in this application acquire real-time operational data from the nuclear power plant and identify the source of the real-time operational data using system identification information. The current target operating mode of the nuclear power plant is determined through the real-time operational data. Then, using the system identification information and the target operating mode, an applicable target rule group is matched from multiple event rule groups constructed based on the content of the technical specifications. Anomalies are identified in the real-time operational data using the target rule groups. When an anomaly is detected, the corresponding target event rule is triggered, and corresponding target processing information can be obtained based on the target event rule. Therefore, this application constructs multiple event rule groups based on the content of the technical specifications, determines the applicable target rule group based on the system identification information and the current target operating mode of the nuclear power plant, and then identifies anomalies in the actual operational data of the nuclear power plant. When a target event rule is triggered, corresponding target processing information is provided. This achieves efficient utilization of the technical specifications.

[0208] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0209] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0210] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0211] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0212] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0213] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0214] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above 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. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between the system or units may be electrical, mechanical, or other forms.

[0215] The units described above 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.

[0216] 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.

[0217] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a 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 multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0218] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. An anomaly handling method based on nuclear power plant technical specifications, characterized in that, The method includes: Acquire real-time operating data of a nuclear power plant; wherein the real-time operating data is configured with system identification information that identifies the source of the data; Based on the real-time operational data, the target operating mode of the nuclear power plant is determined; Based on the system identification information and the target operation mode, an applicable target rule group is matched from a plurality of preset event rule groups; wherein, the event rule group is constructed based on the content of the technical specification. Based on the target rule group, anomalies are identified in the real-time running data, and target processing information corresponding to the triggered target event rule is obtained according to the triggered target event rule. Each triggered target event rule is converted into an abnormal trigger event and stored in an event record database to obtain historical event data. The process of identifying anomalies in the real-time running data based on the target rule group and obtaining target processing information corresponding to the triggered target event rule includes: determining the association range in the historical event data based on the target rule group to obtain a potential association event group; identifying anomalies in the real-time running data to obtain the triggered target event rule and generating a corresponding target trigger event; performing association matching based on the target trigger event in the potential association event group to obtain multiple target association events; and obtaining corresponding target processing information based on the target trigger event and the multiple target association events. The step of performing association matching based on the target triggering event in the potential associated event group to obtain multiple target associated events includes: determining the event triggering factor and event triggering time corresponding to the target triggering event through the target event rule corresponding to the target triggering event; and determining multiple associated target associated events from the potential associated event group based on the event triggering factor and the event triggering time. Before performing association matching based on the target triggering event in the potential associated event group to obtain multiple target associated events, the method further includes constructing an event association network, specifically including: obtaining the event triggering time and event triggering factors corresponding to each of the abnormal triggering events; and constructing a knowledge graph for each of the corresponding abnormal triggering events based on the event triggering time and the event triggering factors to obtain the event association network.

2. The method according to claim 1, characterized in that, Before matching an applicable target rule group from a set of preset event rule groups based on the system identification information and the target operating mode, the method further includes constructing the event rule group based on the technical specifications, specifically including: The technical specifications are processed by character recognition to obtain the text data of the specifications. Based on the textual data in the specification, the scope of application is parsed to obtain multiple rules and their applicable conditions. The event rule data is obtained by parsing the text data in the specification document. The event rule data is grouped based on the applicable conditions information of each rule to obtain the corresponding event rule group.

3. The method according to claim 2, characterized in that, The step of parsing rule events based on the textual data in the specification to obtain event rule data includes: Based on the text data in the specification document, the text content is parsed to obtain the operating constraints, solution plan information, event handling time, and anomaly check frequency; wherein, the event handling time represents the time allocated to resolve anomalies, and the anomaly check frequency represents the frequency of proactively checking the equipment or functions. Anomaly detection conditions are generated based on the aforementioned operational constraints; An anomaly handling plan corresponding to the operational constraints is generated based on the aforementioned measures and plans information; Anomaly event rules are constructed based on the anomaly determination conditions, the anomaly handling scheme, the event handling time, and the anomaly checking frequency. All the aforementioned abnormal event rules are integrated to obtain event rule data.

4. The method according to claim 1, characterized in that, After obtaining the corresponding target processing information based on the target triggering event and multiple target-related events, the process further includes: Based on the triggered target event rules, generate the target triggered event configured with an event priority label, event trigger time, and event processing time; The severity rating of the target triggered event is obtained by rating the severity of the event based on the event priority label, the event trigger time, and the event processing time. Based on the event urgency rating and multiple target-related events, a urgency level rating is obtained to arrive at a comprehensive urgency rating. An event warning is issued based on the comprehensive crisis rating.

5. The method according to claim 1, characterized in that, After identifying anomalies in the real-time running data based on the target rule group and obtaining target processing information corresponding to the triggered target event rule, the process further includes evaluating the target processing information, specifically including: Based on the target processing information, anomaly repair processing is performed on the target triggering event that triggers the target event rule to obtain anomaly repair results; An anomaly repair evaluation is performed based on the anomaly repair results to obtain rule feedback information; The target event rule is iterated based on the rule feedback information to obtain the target modification rule.

6. The method according to claim 5, characterized in that, The step of iterating the target event rule based on the rule feedback information to obtain the target modification rule includes: Based on the target modification rules, a simulated environment test is performed to obtain rule test information; Based on the rule test information, add the target modification rule to the target rule group.

7. The method according to claim 1, characterized in that, Based on the real-time operational data, the method for determining the target operating mode of the nuclear power plant, and in cases where the nuclear power plant needs to switch operating modes, further includes: Receive an operation mode switching request and obtain the operation mode to be switched to; Based on the operating mode to be switched, a suitable preliminary rule group is matched from a set of preset event rule groups; An anomaly correlation assessment is performed based on the preparatory rule group and the triggered target event rules to obtain mode switching suggestions; Execute the operation mode switching request according to the mode switching suggestion.

8. The method according to claim 7, characterized in that, The abnormal correlation evaluation based on the pre-set rule group and the triggered target event rule, to obtain mode switching suggestions, includes: If the abnormal event rules included in the preparatory rule group are related to the already triggered target event rule, a mode switching suggestion that does not recommend switching the operating mode is generated. If the abnormal event rules included in the preparatory rule group are not associated with the triggered target event rules, a mode switching suggestion is generated to recommend switching the operating mode.

9. An anomaly handling system based on nuclear power plant technical specifications, characterized in that, The system includes a data acquisition module, a rule application scope determination module, an operation mode recognition module, and an anomaly recognition and processing module. The data acquisition module is used to acquire real-time operating data of the nuclear power plant; wherein, the real-time operating data is configured with system identification information that identifies the source of the data; The operation mode identification module is used to determine the target operation mode of the nuclear power plant based on the real-time operation data. The rule scope determination module is used to match an applicable target rule group from a set of preset event rule groups based on the system identification information and the target operation mode; wherein, the event rule group is constructed based on the content of the technical specification. The anomaly identification and processing module is used to identify anomalies in the real-time running data based on the target rule group, and obtain target processing information corresponding to the triggered target event rule according to the triggered target event rule. Each triggered target event rule is converted into an anomaly triggering event and stored in an event record database to obtain historical event data. The process of identifying anomalies in the real-time running data based on the target rule group and obtaining target processing information corresponding to the triggered target event rule includes: determining the association range in the historical event data based on the target rule group to obtain a potential association event group; identifying anomalies in the real-time running data to obtain the triggered target event rule and generating a corresponding target triggering event; performing association matching based on the target triggering event in the potential association event group to obtain multiple target association events; and obtaining corresponding target processing information based on the target triggering event and the multiple target association events. The step of performing association matching based on the target triggering event in the potential associated event group to obtain multiple target associated events includes: determining the event triggering factor and event triggering time corresponding to the target triggering event through the target event rule corresponding to the target triggering event; and determining multiple associated target associated events from the potential associated event group based on the event triggering factor and the event triggering time. Before performing association matching based on the target triggering event in the potential associated event group to obtain multiple target associated events, the method further includes constructing an event association network, specifically including: obtaining the event triggering time and event triggering factors corresponding to each of the abnormal triggering events; and constructing a knowledge graph for each of the corresponding abnormal triggering events based on the event triggering time and the event triggering factors to obtain the event association network.

10. The system according to claim 9, characterized in that, The system also includes a rules database and an event log database; The rule database is used to store the abnormal event rules in the event rule group; The event record database is used to store abnormal triggering events transformed according to the triggered target event rules, and to record the event priority tag, event trigger time and event processing time corresponding to the target event rules.

11. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the anomaly handling method based on the nuclear power plant technical specifications as described in any one of claims 1 to 8.

12. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the exception handling method based on the nuclear power plant technical specifications as described in any one of claims 1 to 8.

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