Nuclear power production work risk identification method based on safety improvement

Abstract

The invention belongs to the technical field of nuclear power production risk identification, and particularly relates to a nuclear power production work risk identification method based on safety improvement. The method comprises the following steps: 1, single system fault modeling: performing task decomposition on a nuclear power production process, performing FMEA analysis on each subtask, listing a single system fault mode, and evaluating an influence level; carrying out HAZOP analysis: identifying a process deviation through a guide word, and quantifying the influence of a single fault on the system; 2, identifying a multi-system coupling path: constructing an inter-system coupling matrix, namely an energy hub available energy coupling matrix M and an energy storage coupling matrix K; 3, dynamic risk visualization is carried out, and a coupling path is converted into a graph structure. The method is suitable for fusing FMEA, HAZOP and multi-system coupling analysis, establishing a risk identification system covering the whole process of equipment maintenance, fuel operation and radiation monitoring, and quantifying a risk propagation path and an influence level.

Description

Technical Field

[0001] This invention belongs to the field of nuclear power production risk identification technology, specifically relating to a method for identifying risks in nuclear power production work based on safety enhancement. Background Technology

[0002] Existing methods for identifying risks in nuclear power production have limitations in multi-system coupled risk analysis, personalized user support, and information display efficiency: First, risk identification dimensions are limited: Traditional FMEA and HAZOP only analyze a single system independently, failing to consider the fault coupling and propagation between multiple systems in nuclear power production, such as power, heat, and radiation monitoring. For example, electrical faults may trigger cascading reactions in the thermal system, leading to incomplete risk assessments. Second, user adaptability is insufficient: Existing systems lack a personalized recommendation mechanism driven by user behavior data, unable to provide customized risk information based on job position (e.g., maintenance personnel, operators), work history (e.g., whether they have handled radiation anomalies), browsing habits, or focus on high-temperature and high-pressure risks). Third, information display is inefficient: Management programs often present large blocks of text with loose logical structures, such as mixing risk points with control measures, making rapid retrieval and memorization difficult, potentially delaying risk response, especially in emergency scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide a risk identification method for nuclear power production based on safety enhancement. This method is applicable to the integration of FMEA, HAZOP and multi-system coupling analysis to establish a risk identification system covering the entire process of equipment maintenance, fuel operation and radiation monitoring, and to quantify the risk propagation path and impact level.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for identifying risks in nuclear power production based on safety enhancement includes the following steps: Step 1: Single-system failure modeling: Decompose the nuclear power production process into tasks and perform FMEA analysis for each sub-task: List single-system failure modes and assess their impact levels; Perform HAZOP analysis: Identify process deviations using guide words and quantify the impact of single failures on the system; Step 2: Multi-system coupling path identification: Construct inter-system coupling matrices—the available energy coupling matrix M for energy hubs and the energy storage coupling matrix K; Step 3: Dynamic risk visualization: Transform the coupling paths into a graph structure.

[0005] Step 1: The production process includes equipment maintenance, fuel handling, and radiation monitoring.

[0006] Step 1: Single system failure modes include loose pipe connections during fuel operation.

[0007] Step 1: The impact level includes minor, severe, and fatal.

[0008] Step 1: The guiding keywords include abnormal traffic and excessive pressure.

[0009] Step 1: Quantify the impact of a single fault on this system: The probability of radiation dose exceeding the standard due to pipeline leakage is 0.3.

[0010] Step 2: Power system faults affect the power of the cooling pumps in the thermal system through the available energy coupling matrix M of the energy hub, resulting in a decrease in cooling efficiency, with a coupling coefficient of 0.8.

[0011] Step 2: Power system faults include voltage drops.

[0012] Step 2: Temperature anomalies in the thermal system affect the accuracy of radiation monitoring equipment through the energy storage coupling matrix K, with a coupling coefficient of 0.5.

[0013] Step 3: The graph structure includes nodes: systems / devices, edges: coupling relationships, and a heatmap to display the risk propagation intensity in real time. Red edges indicate high-risk coupling paths.

[0014] The beneficial effects achieved by this invention are as follows: Step 1 of this invention: Single-system fault modeling integrating FMEA and HAZOP. First, the nuclear power production process—equipment maintenance, fuel operation, radiation monitoring, etc.—is decomposed into tasks. FMEA analysis is carried out for each sub-task to list single-system fault modes, such as loose pipe joints in fuel operation, and to assess the minor, severe, and fatal impact levels. Then, HAZOP analysis is performed to identify process deviations through guide words such as abnormal flow and excessive pressure, and to quantify the impact of a single fault on the system. For example, the probability of radiation dose exceeding the standard due to pipe leakage is 0.3, thus achieving accurate risk identification from the single-system dimension.

[0015] Step 2 of this invention: Multi-system coupling path identification. By constructing inter-system coupling matrices—the available energy coupling matrix M of the energy hub and the energy storage coupling matrix K—the coupling coefficients and propagation paths of faults in different systems are quantified. For example, a sudden voltage drop in the power system affects the cooling pump power of the thermal system through the available energy coupling matrix M of the energy hub, resulting in a decrease in cooling efficiency; an abnormal temperature in the thermal system affects the accuracy of radiation monitoring equipment through the energy storage coupling matrix K. This solves the problem of traditional methods ignoring the risks of multi-system coupling.

[0016] Step 3 of this invention: Dynamic risk visualization presentation, transforming the coupling path into a graph structure—with systems / equipment as nodes and coupling relationships as edges, the intensity of risk propagation is displayed in real time through a heat map, such as using red edges to represent high-risk coupling paths, replacing traditional long text descriptions and improving the efficiency of information acquisition in emergency scenarios.

[0017] In summary, this invention breaks through the traditional rule-based recommendation model by automatically mining the implicit correlation between user behavior and risk knowledge through graph neural networks, realizing a dual recommendation logic of data-driven and domain knowledge-based approaches. Addressing the scarcity of labeled risk data in the nuclear power field, it automatically mines potential risk patterns in historical operations through unsupervised learning, expanding the coverage of the risk knowledge graph. Combined with the three core implementation steps mentioned above, it comprehensively improves the comprehensiveness, accuracy, and emergency response efficiency of risk identification in nuclear power production. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a safety-enhanced approach to risk identification in nuclear power production. Figure 2 Flowchart for modeling risks associated with multi-system coupling; Figure 3 Flowchart for creating user profiles; Figure 4 Flowchart for enhancing unlabeled data. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown in Figure 4, the steps of a method for identifying risks in nuclear power production based on safety enhancement are as follows: Step 1: Single System Fault Modeling (FMEA + HAZOP) The nuclear power production process—equipment maintenance, fuel operation, and radiation monitoring—is decomposed into tasks. FMEA analysis is performed for each sub-task: single-system failure modes are listed, such as loose pipe joints during fuel operation, and the impact level is assessed—minor, severe, or fatal; HAZOP analysis is conducted: process deviations are identified using guide words, such as abnormal flow and excessive pressure, and the impact of single failures on the system is quantified. For example, the probability of radiation dose exceeding the standard due to pipe leakage is 0.3; abnormal temperature in the thermal system affects the accuracy of radiation monitoring equipment through the energy storage coupling matrix K, with a coupling coefficient kH−R=0.5. Step 2: Identification of Multi-System Coupling Paths Constructing inter-system coupling matrices—the energy hub's available energy coupling matrix M and the energy storage coupling matrix K—is crucial. For example, a power system fault—a voltage drop—affects the cooling pump power of a thermal system through the energy hub's available energy coupling matrix M, leading to a decrease in cooling efficiency, with a coupling coefficient kE−H=0.8. A thermal system temperature anomaly affects the accuracy of radiation monitoring equipment through the energy storage coupling matrix K, with a coupling coefficient kH−R=0.5.

[0021] Step 3: Dynamic Risk Visualization The coupling path is transformed into a graph structure—nodes: systems / devices, edges: coupling relationships. The intensity of risk propagation is displayed in real time through a heatmap, such as red edges representing high-risk coupling paths.

[0022] This invention integrates FMEA and HAZOP for single-system fault modeling: For each sub-task in nuclear power production, FMEA analyzes single-system fault modes and impact levels, while HAZOP identifies process deviations and quantifies the impact of single faults on the system. Multi-system coupling path identification: An energy coupling matrix M and an energy storage coupling matrix K are constructed for energy hubs between systems, quantifying the coupling coefficients and propagation paths of faults in different systems, such as power and heat, and heat and radiation monitoring systems. Dynamic risk visualization: Coupling paths are transformed into graph structures, displaying risk propagation intensity in real-time using heatmaps, intuitively presenting high-risk paths. This invention breaks through the traditional rule-based recommendation model, automatically mining the implicit correlation between user behavior and risk knowledge through graph neural networks, realizing a dual recommendation logic of data-driven + domain knowledge, solving the problem of scarce labeled risk data in the nuclear power field, and automatically mining potential risk patterns in historical operations through unsupervised learning, expanding the coverage of the risk knowledge graph. Replacing traditional long text descriptions, key information is presented in tabular form, and coupling paths are visualized graphically, improving the efficiency of information acquisition in emergency scenarios.

Claims

1. A method for identifying risks in nuclear power production operations based on safety enhancement, characterized in that: Step 1: Single System Failure Modeling: Decompose the nuclear power production process into tasks and perform FMEA analysis for each sub-task: List single system failure modes and assess their impact levels; perform HAZOP analysis: Identify process deviations using guide words and quantify the impact of single failures on the system; Step 2: Multi-System Coupling Path Identification: Construct inter-system coupling matrices—the available energy coupling matrix M for the energy hub and the energy storage coupling matrix K; Step 3: Dynamic Risk Visualization: Transform coupling paths into graph structures.

2. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 1: The production process includes equipment maintenance, fuel handling, and radiation monitoring.

3. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 1: Single system failure modes include loose pipe interfaces during fuel operation.

4. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 1: The impact level includes minor, severe, and fatal.

5. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 1: The guiding words include abnormal traffic and excessive pressure.

6. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 1: Quantify the impact of a single fault on the system: The probability of radiation dose exceeding the standard due to pipeline leakage is 0.

3.

7. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 2: Power system faults affect the power of the cooling pump in the thermal system through the available energy coupling matrix M of the energy hub, resulting in a decrease in cooling efficiency, with a coupling coefficient of 0.

8.

8. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 7, characterized in that: Step 2: Power system faults include voltage drops.

9. The method for identifying risks in nuclear power production operations based on safety enhancement as described in claim 1, characterized in that: Step 2: Temperature anomalies in the thermal system affect the accuracy of radiation monitoring equipment through the energy storage coupling matrix K, with a coupling coefficient of 0.

5.

10. The method for identifying risks in nuclear power production operations based on safety enhancement according to claim 1, characterized in that: Step 3: The graph structure includes nodes: systems / devices, edges: coupling relationships, and a heat map to display the risk propagation intensity in real time. Red edges indicate high-risk coupling paths.

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